Asymmetry and Aggregation in the EU

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Copyright material from www.palgraveconnect.com - licensed to ETH Zuerich - PalgraveConnect - 2011-04-21

Asymmetry and Aggregation in the EU

10.1057/9780230304642 - Asymmetry and Aggregation in the EU, David G. Mayes and Matti Virén

Also by David G. Mayes TOWARDS A NEW FRAMEWORK FOR FINANCIAL REGULATION (Co-authored) DESIGNING CENTRAL BANKS (Co-authored) THE MICROFOUNDATIONS OF ECONOMIC SUCCESS: Lessons from Estonia PROBLEMS OF GOVERNANCE IN THE EUROPEAN UNION: Migration, Monetary Integration, Socio-Economic Change and Trade THE VARIETIES OF LINGUISTIC, RELIGIOUS AND GEOGRAPHICAL IDENTITIES IN EUROPE: Essays on the Problem of European Integration DEPOSIT INSURANCE (Co-authored) OPEN MARKET OPERATIONS (Co-authored) PROSPECTS FOR FINANCIAL MARKETS THE STRUCTURE OF FINANCIAL REGULATION (Co-authored) ADJUSTING TO EMU (Co-authored) NEW ZEALAND AND EUROPE: Connections and Comparisons (Co-authored) WHO PAYS FOR BANK INSOLVENCY? (Co-authored) SOCIAL EXCLUSION IN EUROPEAN WELFARE STATES (Co-authored) SOCIAL EXCLUSION AND EUROPEAN POLICY (Co-authored) THE EVOLUTION OF THE SINGLE EUROPEAN MARKET (Co-authored) SOURCES OF PRODUCTIVITY GROWTH (Co-authored) THE SINGLE MARKET PROGRAMME AS A STIMULUS TO CHANGE: Comparisons between Britain and Germany (Co-authored) INEFFICIENCY IN INDUSTRY (Co-authored) THE EVOLUTION OF RULES FOR A SINGLE EUROPEAN MARKET: VOL 1 Industry and Finance VOL 2 Rules Democracy and the Environment VOL 3 Social and International Issues FOREIGN DIRECT INVESTMENT AND TRANSITION: The Case of the Visegrad Countries (Co-authored) THE EXTERNAL IMPLICATIONS OF EUROPEAN INTEGRATION (Co-authored) PUBLIC INTEREST AND MARKET PRESSURES: Problems for the 1992 Programme (Co-authored) ACHIEVING MONETARY UNION (Co-authored) A NEW STRATEGY FOR SOCIAL AND ECONOMIC COHESION AFTER 1992 (Co-authored) THE EUROPEAN CHALLENGE: Industry’s Response to the 1992 Programme (Co-authored) A STRATEGY FOR THE ECU (Co-authored) SHARPBENDERS: The Secrets of Unleashing Corporate Potential (Co-authored) INTEGRATION AND EUROPEAN INDUSTRY (Co-authored) THE EXCHANGE RATE ENVIRONMENT (Co-authored) MODERN PORTFOLIO THEORY AND FINANCIAL INSTITUTIONS (Co-authored) APPLICATIONS OF ECONOMETRICS THE PROPERTY BOOM INTRODUCTORY ECONOMIC STATISTICS (Co-authored)



IMPROVING BANKING SUPERVISION (Co-authored)

Asymmetry and Aggregation in the EU David G. Mayes and


Matti Virén


© David G. Mayes and Matti Virén 2011 All rights reserved. No reproduction, copy or transmission of this publication may be made without written permission. No portion of this publication may be reproduced, copied or transmitted save with written permission or in accordance with the provisions of the Copyright, Designs and Patents Act 1988, or under the terms of any licence permitting limited copying issued by the Copyright Licensing Agency, Saffron House, 6–10 Kirby Street, London EC1N 8TS. Any person who does any unauthorized act in relation to this publication may be liable to criminal prosecution and civil claims for damages. The authors have asserted their rights to be identiﬁed as the authors of this work in accordance with the Copyright, Designs and Patents Act 1988. First published 2011 by PALGRAVE MACMILLAN Palgrave Macmillan in the UK is an imprint of Macmillan Publishers Limited, registered in England, company number 785998, of Houndmills, Basingstoke, Hampshire RG21 6XS.

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Contents List of Figures

vi

List of Tables

viii

List of Abbreviations

x

Acknowledgements

xi

Preface

xii

The Nature of Asymmetry

1

2

Estimation and Aggregation Concerns

10

3

Aggregate Supply and Demand in an Open Economy

42

4

The Phillips Curve

77

5

Regional and Sectoral Concerns

115

6

Output, Unemployment and the Labour Market: The Okun Curve

134

7

Asymmetry and the Role of the Public Sector

158

8

Monetary Policy

174

9

Fiscal Responses

204

References

219

Index

232

v



1

List of Figures Illustration of a simple threshold model with cyclical data A comparison of a rigid and smooth threshold An effect of smoothing on a two threshold model Illustration of the minimum conditions Friedman’s plucking model Kernel densities of output growth with the EU27 data for 1990–2008 2.7 Implications of the convexity of the Phillips curve 2.8 An effect of heterogeneity of estimation results 2.9 Kalman filter estimates of threshold model coefficients 2.10 Distribution of coefficients 2.11 Comparison of cross-section variance of GDP growth rates and the squared growth rate of EU27 GDP 3.1 Estimated country-specific interest rate effects from an IS curve 3.2 Sectoral coefficients of the real interest rate 3.3 Coefficients of the real exchange rate 3.4 Median of key macro variables before and after the EMU 3.5 Similarities in (a) growth and (b) inflation performance in the euro area and the US 3.6 Scatter plot between change rates of house and stock prices 3.7 The growth rate of (real) house and stock prices (medians) 3.8 Median values of output growth and output gap 3.9 Comparison of GDP growth rates and inflation over 15 EU countries 4.1 Indicators of output (medians) 4.2 Comparison of inflation rates (medians) 4.3 Expected inflation 4.4 Coefficient of unemployment in a New Keynesian Phillips curve 4.5 Estimates of simple nonlinear Phillips curve for the EU for the pre-euro period 4.6 Linearity and nonlinearity in the Phillips curve and the setting of policy 4.7 Real time OECD output gap estimates 1994–2002 vi


19 21 22 25 27 28 30 32 34 37 41 52 53 53 54 56 58 59 62 69 86 87 90 91 92 96 102


2.1 2.2 2.3 2.4 2.5 2.6

List of Figures vii

5.6 5.7 6.1 6.2 6.3 6.4 6.5 6.6 7.1

7.2 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 9.1 9.2 9.3

Real time HP filtered output gaps Real time and revised HP filtered output gaps Comparison of unemployment dispersion measures Sectoral output growth dispersion indicators The extent of change Evolution of sectoral shares (median values) Comparison of income (per capita) and unemployment dispersion Impulse responses of the VAR model Finnish data on regional unemployment rates Convergence of unemployment in the EU Comparison of income (per capita) and unemployment dispersion Indicators of output Country-specific coefficients of a threshold model for the Okun curve Variance of the unemployment rate across countries Variance of GDP growth across countries Long-run effect of a 1 per cent increase in public consumption on government surplus/GDP with and without policy coordination Comparison of expansive fiscal policy effects in the euro area Interest rate forecast for the euro period An FCI for the euro area An FCI for Germany An FCI for the UK An FCI for the euro area with an increased weight on the real exchange rate The real interest rate, real exchange rate and asset prices in the euro area The impact of house prices on a country by country basis Confidence and house prices An EU average of the impulse responses of d/y* to growth shocks Median of fiscal variables before and after the EMU Change in the responsiveness to the debt ratio

107 108 121 125 125 126 128 130 131 146 147 148 149 149 150 170

171 193 194 195 195 196 197 198 202 210 212 215



4.8 4.9 5.1 5.2 5.3 5.4 5.5

List of Tables LR test results for a threshold model Mean values of ˆc1 and cˆ2 from the simulated data Summary of Granger and Lee findings Estimates of the ratio of the real interest rate to the real exchange rate effect from Mayes and Virén (2000) 3.2 OLS estimation results for the 1987:1–1997:4 period 3.3 IS curves (basic specification) 3.4 Estimation of an extended IS curve 3.5 Basic IS curve specification with different lags 3.6 Comparison of different IS curve specifications 3.7 Estimation of the IS curve with the output gap variable 3.8 Asymmetry in the IS curve 3.9 The effect of house and stock prices: An update 3.10 Euler equations for output using the Consensus Forecast data 3.11 Estimation of a ‘consumption function’ 4.1 Phillips curves 4.2 GMM estimates of a New Keynesian Phillips curve 4.3 When did the EMU show up? 4.4 Estimates of a Phillips curve with the OECD forecast data 4.5 Correlations and test for unbiasedness 4.6 Estimates of restricted Hybrid model with HP filtered output gap, 1977–2006 4.7 Estimates of restricted Hybrid model, 1994–2006 4.8 Estimates of the hybrid model, GMM, 1977–2006 5.1 Phillips curves from regional data 5.2 Backward-looking Phillips curve with regional data 5.3 Estimates using unemployment rather than the output gap 5.4 Estimates of a nonlinear Phillips curve with output dispersion variables 5.5 Sectoral shares of output over countries and time (1991–2008) 5.6 Estimates of a VAR model with the unemployment dispersion variable 5.7 Finnish regional data results viii


35 38 39 47 48 49 50 60 61 63 66 67 71 74 84 88 89 90 104 110 111 112 120 122 123 124 127 129 132


2.1 2.2 2.3 3.1

List of Tables ix

7.2 7.3 8.1 8.2 8.3 8.4 8.5 8.6 8.7 8.8 8.9 9.1 9.2

The Okun curve Individual country estimates of a nonlinear Okun curve Threshold model estimation results G/Y as the threshold variable Estimation results with panel data A summary of the public consumption simulation Estimates of the Taylor rule Autocorrelation of inflation Threshold model estimates for the Taylor rule Simultaneous system estimation from panel data Reaction function estimates Corridor reaction functions Impact of house and stock prices on interest rates Estimates of alternative interest rate equations The effect of house and stock prices: An update Selected country-specific estimates of equation (9.1) Evidence of changing fiscal behaviour

139 142 162 164 169 179 181 181 183 184 185 190 192 200 207 214 Copyright material from www.palgraveconnect.com - licensed to ETH Zuerich - PalgraveConnect - 2011-04-21

6.1 6.2 7.1


ARIMA BDS BEPG CPI DSGE EEA ECB ECOFIN EDP EMU EMS EU ERM FCI FE FRB GDP GLS GMM HP IV LM LR LS MCI NiGEM NUTS OECD OLS REH SGP SUR STR VAR

Autoregressive Integrated Moving Average Brock Dechert Scheinkman Broad Economic Policy Guidelines Consumer Price Index Dynamic Stochastic General Equilibrium European Economic Area European Central Bank European Council of Finance Ministers Excessive Deficit Procedure Economic and Monetary Union European Monetary System European Union Exchange Rate Mechanism Financial Conditions Index Fixed Effects Federal Reserve Board Gross Domestic Product Generalised Least Squares Generalised Method of Moments Hodrick–Prescott Instrument Variable Lagrange Multiplier Likelihood Ratio Least Squares Monetary Conditions Index National Institute Global Econometric Model Nomenclature of Territorial Units of Statistics Organisation for Economic Cooperation and Development Ordinary Least Squares Rational Expectations Hypothesis Stability and Growth Pact Seemingly Unrelated Regression Smooth Transition Regression Vector Autoregression

x



List of Abbreviations

Many people have contributed to our understanding of the problems discussed in this book over the last 25 years, too many to name them all. We should however mention Maritta Paloviita who has participated in joint research with both of us, as we use some of the results from that work. Pentti Saikkonen from the University of Helsinki provided useful advice on modelling issues, Jan Fidrmuc, Prasanna Gai, Alfred Guender, Mark Holmes, Dimitri Margaritis, Alberto Montagnoli and Brian Silverstone all provided very helpful advice on an earlier draft of this book at a workshop at the University of Auckland and we thank the Andrew Shonfield Association for providing the finance for the occasion. All of the joint research in the book was undertaken while we were both at the Bank of Finland, Mayes as Advisor to the Board and Virén as Research Consultant. Juha Tarkka as Head of the Research Department in the early years did much to encourage the work. However, many of our colleagues participated in seminars and conferences in which the work was presented along the way. We have pillaged our previous publications in putting the book together, but most of the results have been reworked on a common dataset that brings the analysis up to the end of 2008. Our thanks go to a string of helpful research assistants who have put the data together. In particular, we thank Heli Tikkunen and Tarja Yrjöla from the Bank of Finland. We are grateful to Janet Mayes to making the corrections to the final typescript, which got rather battered with two authors on opposite sides of the world. Through much of the period when we were working the downside of experience was not prominent and interest was polite rather than enthusiastic. With the global financial crisis the relevance of our work has become only too obvious and we regret that we were not more influential earlier.

xi



Acknowledgements

This book has been a long time in the making. We began writing separately on asymmetry over 20 years ago and jointly in the late 1990s. We had also been approaching the topic from different directions. Our joint interest arose from concern over Finland’s transition to the euro area and how euro area monetary policy might be put together for the area as a whole. Since there was the potential for considerable variation among the member states over where they lie in the economic cycle and over the value of the parameters of key economic relationships it appeared to us that there was likely to be a great deal of difference between aggregating the data and estimating euro wide relationships and aggregating the results from estimating the impact on individual countries. Little prior work had been done and the preference at the time was to estimate a new area-wide model. We thought the consequences should at least be explored. As the work developed we realised that the extent of asymmetry in the euro economies was quite complex, relating not just to the behaviour of aggregate output but to the labour market, inflation and above all to policy itself both with regard to the fiscal stance and to monetary policy. This indeed became clear. However, our work coincided with the period of the ‘great moderation’ so that the consequences became less obvious. With the global financial crises the extent of the asymmetry and indeed the differences among the euro area countries has become very obvious, to the extent that it has placed strains on the area and required the building up of a major fund to help the worst affected countries through their difficulties. Our analysis suggests that by not aggregating the asymmetric relationships monetary policy may tend to underestimate the lack of downward pressure from the countries in relative difficulty. It is noticeable that over much of the first decade of the euro area, that inflation turned out to be a little higher than wanted. Perhaps asymmetry made a contribution to this. Just the same asymmetries exist outside the euro area but there the exchange rate can vary to cope with asymmetric shocks and asymmetric responses. This is not to suggest that membership of the euro area was a mistake for some of the countries that are most different from the general movement, but that they need an adequate cushion to cope with the harsh adjustment. Among the euro countries, Finland xii



Preface

made among the greatest preparations, with firm fiscal consolidation over the course of a decade and with the setting up of buffer funds for both unemployment and pensions. Now the shock has come and Finland has been one of the most affected countries as a result of the importance of investment goods in its export markets. Nevertheless, the country appears to be getting through such a sharp recession without a threat to long-run sustainability and without an unreasonable burden on future generations. In retrospect the provision of the Stability and Growth Pact both to encourage fiscal consolidation and to try to guard against the attempt to be overconfident in expansions so that contractions build up too great budgetary deficits has proved to be very wise. It is a pity that it was not followed more enthusiastically in some cases. Work on asymmetry will be given a considerable boost by the crisis, not simply because its importance has become clear but because we now have many more observations on how people behave when there is a serious adverse shock and financial uncertainty. The extent of the difference is clear, with the temporary closure of some financial markets. While most of our work was initially published before the crisis our compiling it now may seem a bit after the event but we hope that the results will be of assistance in enabling policy to be sufficiently asymmetric in future that the difficulties are better offset, particularly in good times when the problems are built up. DAVID G MAYES MATTI VIRÉN



Preface xiii


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1

The global financial crisis has been a vivid reminder of how asymmetric economic behaviour is. Recessions in an economy do not have the same pattern as expansions. Expansions are less sharp and last longer. In part they reflect preferences. Macroeconomic policy seeks to encourage and prolong expansions but seeks to make recessions as shallow and short as possible. We thus see asymmetry in both economic behaviour and in economic policy. While the asymmetry in the macroeconomy is very obvious the same asymmetry can be found in microeconomic and sectoral behaviour as well. A well-known example comes from consumption. When incomes rise, all but the poor spend much of the increase but save the rest. The proportions vary according to whether they expect this increase to be one off or enduring. However, if incomes fall by the same amount people resist seeing their consumption fall, particularly if the shock is expected to be temporary. In the longer term consumption will fall as the ability to dissave or borrow is inhibited but the pattern of behaviour is clearly asymmetric. More trivially there are many actions that are not reversible because of experience. However much of economic analysis largely ignores the existence of this asymmetry and hence most models are symmetric in character. Such asymmetries are of limited importance when discussing marginal changes but are much more important when the shifts are large, as recently, or where they very obviously represent a regime change. The failure to anticipate the consequences of the events that led to the present crisis have generated a burst of enthusiasm in investigating this phenomenon. We have been working in this area for many years and have taken this opportunity to put together a range of these ideas in a single volume. 1



The Nature of Asymmetry

In this book we look at various aspects of asymmetry in the European economy and explore the consequences for policy. Our reason for picking Europe is two-fold. First, trivially, because this is the region on which we have done most work but, second, because it offers an extra feature that makes the policy consequences more interesting. In any event macroeconomic policy applied to a country as a whole averages out over the needs of the households, firms and regions within it. But in the European Union (EU) and in the euro area in particular, a single monetary policy is run for a large and relatively disparate set of countries. Averaging across countries would not matter very much if the disparities were relatively small. But if they are large and the asymmetries are important then this can have important consequences for policy. Take a simple Phillips curve for example. When there are strong demand pressures, say with a clearly positive output gap, changes in demand will have a considerable effect on inflation. The same changes in demand, when the output gap is the same size but negative, may have almost no impact on inflation. Simple arithmetic averaging could, therefore, give a misleading implication for the single policy. The positive and the negative do not simply cancel out. In the EU cross-border fiscal transfers to help offset the unequal impact of shocks and other policies are trivial. In individual countries they are extensive, hence asymmetry has greater consequences for the EU than elsewhere, particularly in the euro area where changes in the nominal exchange rate among the members is not possible. In the chapters that follow we begin by setting out what we understand by asymmetry and then how it might be measured before exploring it in detail in a number of areas relating to inflation, unemployment, growth, fiscal and monetary policy. We build up the analysis into a small model of the economy that we can use for analysing the policy problems. We do not pretend to offer a complete treatment and one area that is ripe for further examination is the behaviour of firms, particularly entry and exit. New firms face a steep learning curve and their productivity improves rapidly as a result. However, that improvement needs to be rapid and many new firms fail quite early in their lives as they are unable to reach the profitable cost structures of their more established competitors before the financing runs out. Declining firms on the other hand tend to decline rather slowly and can continue in business for a considerable length of time. Unlike new firms which have had to make all their physical and productive investment, whether in their staff or processes, up front and have to bear the costs of this before the sales income starts, mature firms have long written off this initial investment and can let their equipment



2 Asymmetry and Aggregation in the EU

The Nature of Asymmetry 3

run down and not retrain their staff. A substantial proportion of their costs are sunk – their experience, their customer and supplier networks etc., with little value in an alternative use. While such actions may merely be postponing the inevitable they can nevertheless be drawn out and there is always the hope of new invention or major new customer to turn things round. Even insolvency is usually put off as creditors hope they can avoid losses. Some of these issues are explored in Mayes (1986) but they require a very different dataset from the macroeconomic information that we use in the present study, so we have to leave them for the future.

Asymmetry and its causes

In our discussion thus far we have mainly been concerned to point out the nature of the asymmetry that we observe. Interest tends to focus on the economy as a whole so let us begin there. Thus Keynes (1936) was initially concerned simply to point out the depth, steepness and duration of the up and down phases of economic cycles were clearly different. The approach of Friedman (1964) goes a little further by describing a ‘plucking’ model. Economies tend to run at full capacity or potential output, where these two are defined as some sort of level of maximal output or output growth beyond which signs of overheating emerge, whether in terms of a sharp rise in costs, breakdown of machinery, or exhaustion of staff, or in terms of inflation. These are often expressed as ‘natural rates’. Operating below this is not only a waste of resources but an unplanned event, generated by adverse shocks. Attempting to run faster than potential is hence bound to be followed by some element of slow down, whereas firms can quickly return to potential once demand picks up, provided the downturn has been short-lived and staff have not been laid off or machinery run down. Hence in this view one can return swiftly to normal like a plucked string. However there will be some overshooting with the initially rapid return to normality tending to go too far. Although the vibration of the strings may not be a good analogy for what tends to happen thereafter. However, these represent observations rather than explanations. As Caballero and Hammour (1994) remark, one of the aspects of asymmetry over the cycle can be seen in the operation of the labour market. In a downturn, job destruction in the sense of people losing their jobs, whether or not the company itself goes out of business, tends to be much more rapid than the process of job creation once the upturn



1.1

begins. Indeed when the former level of output is regained it is likely to be at a noticeably lower level of employment. It is only possible to regain the same levels of employment after a much longer lag. Here explanations can be quite straightforward. It is the less productive staff and less productive firms where the job losses are concentrated. Furthermore, making people redundant is an unpleasant and indeed costly business that is often contested. Hence initially it is avoided and contraction achieved through ‘natural wastage’ and not hiring new staff. Once it is embarked upon it is done to fullest plausible extent as a second round would simply add to the costs and the loss of morale. Some of the redundancies can always be rescinded. Once the economy starts to recover only some of the jobs will be in the firms that have contracted others will be in new firms and in the production of new goods and services. New firms tend to be more productive than existing firms just as exiting firms tend to be less productive (Mayes, 1996). Given their extra costs, new firms have to have a competitive edge. In any case the recovery is likely to require new investment and most new equipment is more productive than that which it replaces. Hence fewer people, although probably more highly skilled, are required. Such investment may well not apply simply to the new production but to the whole of the firm’s output, thus emphasising the contrast. In what follows we look at asymmetry in three key areas of the economy, aggregate supply and demand (Chapter 3), the relation between the extent of pressure in the aggregate economy and in employment and inflation – the Phillips curve – (Chapters 4 and 5) and the relation between output and unemployment – the Okun curve (Chapter 6). In the case of both the Phillips curve and the Okun curve we look at behaviour at the sectoral and regional level. One particular feature worthy of note is that the greater the variance or range of unemployment the greater the impact on inflation. Between them these three areas cover the aggregate asymmetry we have just described. However, this is only part of the story, as the response of policy to this underlying asymmetry in the economy is itself asymmetric. Depending on its nature this asymmetric response can reduce or exacerbate the asymmetry in behaviour. Take the case of monetary policy. In writing their assessment of the Greenspan years, Blinder and Reis (2005) characterised the ‘Greenspan Standard’ as a cautious approach to excess demand pressure and rises in asset prices in particular, provided they did not provide a threat to overall inflation, but a vigorous response to serious downturns to avoid the threat of damaging debt-deflation spirals and the danger of approach-




ing the zero bound for nominal interest rates after which different and probably less effective monetary policy tools have to be used. Up until the boom and bust of the present crisis this approach received considerable support because it is very difficult to decide what constitutes an asset price bubble or an unsustainable increase in the rate of economic growth. No central banker wants to be responsible for halting a sustainable improvement in economic performance so the likely response is sceptical caution, falling short of outright halting of the process. To continue the reference to Alan Greenspan – his famous remark about irrational exuberance in the US stock market was made in December 1996 well before the peak of the dot.com boom in 2000 (Greenspan, 1996). With the benefit of hindsight his caution proved justified but markets ignored the advice and the Federal Reserve did not follow it through with any firm action. We look at this from the point of view of monetary policy in Europe, where there was no such espousal of an asymmetric approach and very little focus on the behaviour of the stock market, yet an exploration of the revealed asymmetry in the setting of monetary policy reveals just the same pattern as in the United States, albeit not so strongly (Chapter 8). Monetary policy does appear to respond a little to both stock and house prices and in an asymmetric manner. However, there may be co-incident factors in recent years as the European Central Bank (ECB) monetary policy also appears to be influenced by monetary policy in the United States, perhaps to reduce the extent of the exchange rate fluctuations that would otherwise emerge if no regard were paid to it. The asymmetry is more complex than simply tending to fight the threat of deflation more vigorously than that of excess inflation. Small fluctuations round the ECB’s objective are not fought with as much vigour as large deviations, irrespective of sign. While asymmetry in monetary policy is of considerable current interest because of its possible contribution to the present crisis, asymmetry in fiscal policy is also marked and in two respects. The first, which we consider in Chapter 7, is simply that increasing the size of the public sector appears to be counter-productive beyond a certain point. At lower ratios of public expenditure to Gross Domestic Product (GDP), increasing the size of the public sector tends to increase the rate of growth, especially when it is focused on infrastructural investment. At higher ratios, as are prevailing at present, the reverse is true and reductions in the size of the public sector are likely to increase the growth rate. We do not speculate on the causes but in combination with the asymmetry in labour markets discussed earlier it is clear that the modern tendency towards




active labour market policies rather than public sector employment in downturns makes a lot of sense. The second issue, addressed in Chapter 9, relates to the SGP (Stability and Growth Pact) among the EU countries and the asymmetry in the treatment of fiscal deficits and surpluses. The SGP is obviously asymmetric, for although it sets a long-term target of debt reduction, it explicitly rules against deficits greater than 3 per cent of GDP, except in hard times such as the present, but has no limit to surpluses. In this chapter we show that this asymmetry is clearly warranted by the asymmetric tendency of policy in the opposite direction. Across the period as a whole, the asymmetric problem has been that governments tend to be overoptimistic about how much they can cut taxation in the up phase of the cycle rather than taking the opportunity for consolidation. Behaviour changed clearly in the run up to the Economic and Monetary Union (EMU) and thereafter. Before that there was also some asymmetry in expenditure but nowadays expenditures tend to be simply counter-cyclical and expenditure reduction was the main tool used to get the member states to conform to the Maastricht criteria and the SGP requirements. We do not consider whether this has changed recently. We explore whether there have been changes in asymmetry as a result of EMU, or to be more literal coincident with the run up to EMU in each of the chapters. Indeed we test more widely for regime changes, particularly since structural breaks are often found around 1985 and 1992. While there are clear differences with respect to policy we also find changes in other sectors of the economy, particularly in regard to the determination of inflation. Before ending these introductory remarks it is worth pointing out that there is no accepted definition of asymmetry and as a result people use the word in a variety of contexts. One which is particularly confusing in the study of Europe is that many people use the word asymmetry when they simply wish to point out that the EU countries are different. Not only is that not the focus of our study but we find that we can pool EU data (with appropriate fixed effects) in almost all cases. There are some outliers and we explore where countries differ from the general run of behaviour.

1.2

Estimating asymmetry and its impact

Perhaps the easiest way to consider these differences in behaviour is just to assume that the coefficients in models will be different in the two




circumstances of growth and recession. This would give us regime switching models, see Holmes and Silverstone (2005). Indeed one might want to argue that there are at least three phases in the business cycle, the downturn, the sharp recovery and the more normal period of growth. But this is not the only way to consider the problem. First of all it may only be some parts of the relationship that are different in the two circumstances, others may remain the same. An alternative is that the distribution of shocks that an economy faces may not always be the same and in recessionary times the economy is subject to downward shocks; see Kim and Nelson (1999), for example. This is a less attractive approach as it has no behavioural component to it. An alternative with a behavioural component to it is one that assumes that normal behaviour is not subject to asymmetry but that the asymmetry occurs in the response to shocks (Enders and Siklos, 2001). In output terms the response to an upward shock will be small (in price terms it will be large) but the response to a downward shock is much larger but eroded quickly (in price terms there will be little impact from a downward shock). An advantage of this model is that it will react in the same manner to further shocks, thus generating primarily a response through inflation on the upside and one through recession on the downside. In this book we primarily use the first approach, considering which variables might be likely to be subject to behavioural change under the two regimes. However, we also consider asymmetry in what is effectively the error correction mechanism. We discuss the methodology used in Chapter 2. One important issue is how the switch between regimes takes place. It could be approached in three obvious ways. One is simply to define a switchover point in terms of points in the cycle or other threshold values of relevant variables, such as the sign of the output gap for example. A second approach would be to look empirically for the changeover point on each occasion or perhaps to use a Markov switching model. Lastly one could assume that the change does not take the form of a simple switch but is progressive, in the form of a smooth transition from one regime to the other. We predominately use the first model in part because this simple approach illustrates the existence of asymmetry very clearly. We do however search for switching points rather than assume that purely arithmetic points, such as whether the economy is growing or contracting, are the only reasonable choice. We also consider the smooth transition model (Granger and Teräsvirta, 1993) in case this is a more realistic representation than the simple model. In some respects we are more inclined to the Sargent (2001) approach which assumes that the changeover between





regimes is different depending upon whether the economy is entering downside or the upside regime. His suggestion is that the switch from good times regime to the recessionary regime is sharp, indeed this loss of confidence is what characterises the sharp decline. On the other hand, as the economy recovers, the move towards the good times regime is more hesitant and progressive. This is in itself would be an explanation of the shape of the economic cycle. Whether inflation and unemployment are subject to the same shape of switch between regimes is a more open question. Sudden bursts of inflation are for example rather more common than sharp declines.

A final remark

Chapter 2 also develops our concern with the problems of aggregation in the EU. Hence before we tackle any of the individual sectors we explore the degree to which the EU economies are different in the major characteristics of the macroeconomy (growth, inflation, unemployment, sectoral composition etc). We also explore the degree to which there has been convergence in the economies as the process of European integration has increased, in particular looking at the changes associated with the advent of EMU. Whether it is because of the deepening integration is difficult to say but there is a clear reduction in dispersion of macroeconomic performance over the period in all respects. The nature of the convergence is however relatively complex. Inflation rates have converged because of a general trend in the developed world towards inflation focused monetary policy. Thus the convergence outside the EU in the rest of the Organisation for Economic Cooperation and Development (OECD) has been similar. Indeed in some cases, such as Ireland, membership of EMU has resulted in an increase in inflation as the common monetary policy has been aimed at the euro area’s overall inflation and not that of the more dynamic parts. Prior to membership Ireland was controlling its own inflation, and indeed all countries needed to do so if they wished to qualify for EMU under the Maastricht criteria in the first place. In the case of unemployment, countries with higher levels of unemployment, such as Finland, were able to bring their unemployment down through faster growth. In some respects therefore there is a trade off between convergence in levels and convergence in rates of change. Convergence in levels necessarily results in diverging in rates of change and vice versa. Our analysis is timely in the sense that the present crisis has illustrated all too vividly how sharply behaviour can change in a recession,



1.3



particularly with the closure of wholesale financial markets when confidence was lost and spreads jumped sharply. However, the analysis is also in a sense premature as the data generated during this period will be the most relevant, given that behaviour over the previous 30 years has been mild by comparison. No doubt we will rework our analysis as the data become available but that must wait for another book when the shape of this cycle is clear.


2

In this chapter we explore first the problems of estimating asymmetry before going on to consider the extent of the aggregation problems that we face in dealing with a group of countries rather than with aggregate data for the EU as a whole. However we begin by summarising the model employed. The individual equations and their justification are developed in subsequent chapters.

2.1

The model in outline

In order to examine asymmetries in macroeconomic behaviour in the EU/EEA we use a simple and very conventional four equation model of the economy, consisting of an IS curve, a Phillips curve, an Okun curve and a monetary policy reaction function that we have employed earlier (Mayes and Virén, 2005).1 We augment this to include fiscal policy, showing how expenditure, revenue and the net position are affected by the business cycle, debt and interest rates. 2.1.1

The IS curve

Following Duguay (1994), Goodhart and Hofmann (2000), the IS curve is of the form ∇yt = a0 + a1∇yt–1 + a2∇yt–2 + a3rrt–i + a4ret–j + a5∇y*t–k + εt

(2.1)

where ∇y is the deviation of output y from its Hodrick–Prescott (HP) filtered trend, rr is the real three-month interest rate (i.e. the nominal 1

A three equation version, omitting the labour market has received considerable attention – Cho and Moreno (2006). 10



Estimation and Aggregation Concerns

rate of interest r less the annual rate of consumer price inflation p), re the real exchange rate (in logs), ∇y* the deviation of OECD output from its HP trend (lag lengths i, j and k typically vary from 1 to 3 quarters in estimation)2 and εt is the error term. Because the construction of output gap is a controversial issue we have also carried out all empirical analysis using the growth rate of output instead (to be denoted as ∆y). As can be seen from the more detailed exposition in Chapter 3, these two measures are far from identical in describing cyclical movements and it is no surprise that the results are somewhat sensitive to the choice of output measure. Fortunately, none of the results is crucially dependent of this choice. The IS curve represents a quite conventional open-economy demand relationship in which real interest rate and real exchange rate represent the main ingredients. The open-economy nature of the equation is further re-enforced by the foreign export demand variables. In addition, we include two wealth (shock) variables, the rate of change of real house prices (hp) and the rate of change of real stock prices (sp). Then the estimating equation reads: ∇yt = a0 + a1∇yt–1 + a2∇yt–2 + a3rrt–i + a4ret–j + a5∇y*t–k + a6hpt + a7spt + εt

(2.1′)

where lag lengths i and j may be different and may also differ over countries. The basic equation is completely backward-looking but we also estimate the model as a hybrid Euler equation for total output which takes the form: ∇yt = a0 + a1∇yt–1 + a2∇eyt+1 + a3rrt–i + a4ret–j + a5∇y*t–k + a6hpt + a7spt + εt

(2.1″)

Estimation of (2.1″) is obviously more complicated because we have to deal with expectations. Then we must either have some (survey) data on expectations or we have to estimate (2.1″) with the joint hypothesis of Rational Expectations which necessitates the use of the

2 As for the real exchange rate, we have used two alternative measures: the real exchange rate in terms of the US dollar and the real effective exchange rate against major currencies.



Estimation and Aggregation Concerns 11


Generalised Method of Moments (GMM).3 Equation (2.1′) is useful also in the sense that it allows to us compute the co-called Financial Conditions Index (FCI). Technically, it can be derived from (2.1′) in the following way: FCIt = (1/aˆ3)(aˆ3rrt–i + aˆ4ret–j + aˆ6hp + aˆ7sp)

(2.2)

where the carets denote estimated values. This index thus reflects the weight of all the financial variables in the model on the real output.4 2.1.2

The Phillips curve

The starting point is the following standard expectations augmented Phillips curve: (2.3)

where ∆p is (actual) inflation, ∆pe is expected inflation, p* is the foreign price level (in domestic currency) and un is unemployment. However, in common with many authors (Galí and Gertler, 1999) we typically use ∇y instead of the unemployment rate, un, to represent the pressure on the economy. This then represents a form of the New Keynesian Phillips curve on the grounds that the output gap may move in step with marginal cost.5 However, in the form set out in (2.3) the curvilinear property of the relationship is largely lost so we augment it to show two facets either side of a threshold, where ∇y+ denotes the values of the output gap that exceed the threshold value τ (if ∇y > τ, ∇y+ = ∇y while if ∇y ≤ τ, ∇y+ = 0). Accordingly ∇y– denotes the remaining values

3 The issue of expectations will be dealt with later on but some comments already merit note here. From the Rational Expectations Hypothesis (REH) point of view ‘actual values’ of future (say, period t+1) inflation deviate from expected inflation by a stochastic disturbance. In a sense, the disturbance is a measurement error. Also survey data probably include a measurement error (in terms of the ‘true’ expected inflation) but this error is not the same as the ‘forecast’ error which is related the REH. This (latter) error ought to be orthogonal to the information set that is used in forming expectations. The corresponding orthogonality conditions can be utilised with the GMM estimator and then no survey data are required. The possible measurement error with the survey data could, in principle, be handled with the Instrument Variable (IV) estimator but it is far from obvious which instruments should be used. 4 We also solve the full model for these effects, which is a more complete procedure than the simple FCI used in the literature. 5 This model is used by Goodhart and Hofmann (2005) successfully in examining both the euro area and the US.



∆pt = b0 + b1∆pt–1 + b2∆pet+1 + b3∆p*t + b4unt + ηt


of ∇y.6 As an alternative, we use the so-called Smooth Transition Regression (STR) model to account for possible nonlinearity of the output effect (see the following section for details). In both cases this allows the impact of output to be different in booms and busts, with the expectation that the effect is stronger when the output gap is positive than when it is negative. In other words b4 > b5 in (2.4).7 (2.4)

When estimating the Phillips curve (2.4) we are usually interested in the parameter values of future and past values of inflation. Even though that is also important from our point of view, the main emphasis here is related to the potentially nonlinear role of the output (marginal cost) variable. Thus, (2.4) is estimated with the various threshold model techniques (see below). Because the Phillips curve is crucially dependent on inflation expectations, there are various problems in estimating the equation (already in the linear form). The conventional solution is to use the GMM estimator and so do we in the current context. As alternatives, we use some survey data and also some (more old-fashioned) backward-looking specifications. As for additional variables, we also experiment with consumption (VAT) taxes. In testing the nonlinearity hypothesis, we mainly use crosscountry panel data which allows for exploiting cross-country differences in labour market conditions (unemployment). More interesting applications can be developed, however, by making use of regional data from the EU. These data include a lot of more variation because intra-country differences in unemployment can be taken into account. For practical reasons, the estimating equation is fitted into crosscountry data but we include intra-country unemployment dispersion variables as additional regressors into (2.3) which now reads: ∆pt = b0 + b1∆pt–1 + b2∆pet+1 + b3∆p*t + b4un+t + b5un–t + b6dispt + ηt

(2.5)

where the disp variable simply reflects either the range or the standard deviation of unemployment rates over regions in a country i. Ideally, 6 Obviously we could have more than two regimes (facets) for ∇y but since we have only limited numbers of observations we use this simple specification (which has been widely used elsewhere, see Yates (1998) for instance). 7 There is some analogy between the Phillips curve and the wage curves and the price equations. Especially with the latter, we have a lot of evidence that (with the micro data) prices tend to increase faster than they fall (for a useful summary, see e.g. Peltzman (2000).



∆pt = b0 + b1∆pt–1 + b2∆pet–1 + b3∆pt* + b4∇y+t + b5∇y–t + ηt


we ought to use regional data for all variables but, unfortunately, regional data are available only for unemployment and (to some extent) output, but not for prices or inflation, or other macro variables such as taxes, exchange rates and so on. 2.1.3

The Okun curve

In its simplest form the Okun curve can be expressed as: ∆Ut = c0 + c1∆yt + εt

(2.6)

where U denotes the unemployment rate and y output. In the empirical analysis we allow population shocks (measured by the working-age population pop) to affect unemployment and introduce simple dynamic adjustment in the form of an error-correction model to derive the following estimating equation: (2.6′)

which reflects an error-correction format.8 Here ∆y is the growth rate in GDP (alternatively, the output gap, ∇y, is used), pop the population of working age and EC the error-correction term (that is lagged by one period) and τ a threshold value for the asymmetry. Prachowny (1993) inter alia argues that some scaling of the labour variable in (2.6) is required so we have also included population of working age in our formulation. Once again we use a threshold approach to the relationship to allow at least some approximation to a curvilinear relationship. Equation (2.6) is estimated using a two-step procedure. First a long-run relationship between employment, output and working-age population is defined and on the basis of this relationship an error-correction term EC is derived. This term is used in the Okun curve specification to take care of long-run relationship between these (quite clearly) nonstationary time series. Thus, the Okun curve is not a simple bivariate relationship but it takes into account possible population shocks and cyclical movements in output. In our mind, the Okun curve is the most obvious candidate for nonlinear economic relationships. If there is any sort of wage-stickiness one might expect that output (demand) changes translate in a different way to changes in employment. Thus, the unemployment effects ought to be larger in depression than in boom periods.

8 The EC term is derived from the level form terms of U, y and pop. In the panel setting, country-specific fixed effect terms are also introduced. See Kiander and Virén (2001) for details of the derivation of this model.



∆U = c0 + c1∆y+(τ) + c2∆y–(τ) + c3 ∆pop + c4EC–1 + τt


2.1.4

The Taylor rule

Finally, we include a monetary reaction function in the form of a Taylor rule rt = θrt–1 + (1 – θ){d0 + d1(πt – πo) + d2(∇yt – ∇yo)}

(2.7)

where the parameter θ permits an element of interest rate smoothing. π is the rate of inflation (which equals ∆p) and π0 its target value. Similarly ∇y0 is the target value of output gap which obviously can be zero (Huang et al., 2001). Rearranging terms leads to the following estimating equation: rt = ρ0 + ρ1rt–1 + ρ2πt + ρ3∇yt + ut

(2.7′)

– allowing for a nonlinear structure in terms of the inflation effects – including the asset prices (more precisely, the change rates of house and stock prices) as additional regressors in the estimated Taylor rule equation. Thus, the final estimating version of the Taylor rule reads: rt = ρ0 + ρ1rt–1 + ρ2π+t + ρ3π–t + ρ4∇yt + ρ5HPt + ρ6SPt + ut

(2.7″)

where HP and SP denote the change rates of nominal house and stock prices. π+ and π– denote inflation rates above and below some critical level (e.g. 2 per cent). Here we have also experimented two thresholds to allow for corridor-type behaviour: interest rates react only to sufficiently large deviations from the target level. The Taylor rules, in the same ways as most economic relationships, could be specified in many different ways, e.g. by using expected inflation instead of actual inflation but here we stick into the most common ways of specifying this and the other three equations. Some differences arise already because of the choice of the inflation variable; we may use either the GDP or consumption deflator, or the CPI (national, or harmonised CPI) in deriving the inflation data. For reasons of robustness checking, we have, in fact, used all these indicators. Equation (2.7′) could be interpreted as some sort of ‘activist’ policy rules where the central bank not only pays attention to price stability (and cyclical situation) but also react to developments in asset prices (state of financial markets). This asset price reaction might just follow



where ρ0 represent the sum of the constant terms. In addition to this standard linear model for interest rates, inflation and output we have estimated the policy rule (2.7) with two extensions:


from the idea that asset prices convey information on future (consumer) price changes but from a more ‘activist’ point of view reactions to asset prices could be motives by a desire to avoid the harmful consequences of asset market bubbles. If that is the case, one might think that the central bank only reacts to substantial deviations from equilibrium levels, that is, to very large cumulative changes in prices. This interpretation would suggest that also the asset price effect is probably nonlinear. As pointed out in Chapter 1, the question whether to react to asset prices is quite controversial. Irrespective of the policy conclusions, it would be useful to test whether in the past, central banks have indeed reacted to financial market developments or not. 2.1.5

The full model

2.1.6

The fiscal sector

We take a straightforward approach to estimating the evolution of public sector balances, revenues and expenditures. (d/y–)t = f0 + f1(d/y–)t–1 + f2t + f3∆y–t + f4∆y+t + f5rt + f6(D/y)t–1 + νt

(2.8)

–

where d is the fiscal measure, y is trend GDP, t is a time trend, r is the nominal interest rate and D government debt. Here the purpose is to test the symmetry of the response in upturns and downturns in the economy. This equation is not fully integrated with the rest of the model, in part because fiscal decision-making tends to follow an annual cycle and hence we do not have exactly matching data.

2.2

Estimation of the model

As mentioned in Chapter 1 there are at least two obvious ways for handling asymmetry in estimation. The first is simply to assume that there are different regimes and that some or all of the parameters differ across regimes. This is the approach of Holmes and Silverstone (2005) to the Okun curve for example. We also need to decide whether there is a simple switch from one regime to the other or whether the transition is progressive. One alternative is to assume that the underlying relationship does not change but it is the response to shocks which varies, dependent either upon whether they are positive or negative, or



This set of equations determines inflation, output, unemployment and the rate of interest. Foreign prices, foreign output and the exchange rate are treated as exogenous to the system. The same is true with asset prices, taxes and the labour force.

upon the type of shock. We use both these approaches but we concentrate on a simplified version of the first, namely to see whether the parameter of key variables is different in the two regimes. This approach is often described as using threshold models. The regimes are determined on the basis of some transition (threshold) variable. The existence of this transition variable is important in distinguishing threshold models from other switching regime models where identification of regimes is based on properties of the model, an issue we return to at the end of this section. One may question the choice of threshold models because within the set of nonlinear models there are various alternatives. The reason becomes clearer if we briefly consider some obvious alternatives but we should stress the fact that we do not only want to test whether there are asymmetries but we want also to have a clear estimate of their nature and magnitude. For this purpose, the threshold model is ideal because the model gives an exact characterisation of regimes as well as the difference in regimes. Thus, such constructions as bilinear models are not very useful.9 This reasoning also means that we are not as such interested in testing the existence of asymmetries although, of course, such testing is no doubt useful and gives guidance for specification and application of nonlinear models.10 The critical values of the transition variable are estimated from the data using some grid search procedure (that minimises the residual sum of squares). We have also made quite extensive use of some intuitively obvious threshold values. Thus, for instance, in the case of the output gap we have used the value of zero, which, in the case of Hodrick–Prescott filter, also comes quite close to the sample average values. Also with the growth rates of output and real asset prices using the zero value as a benchmark makes the interpretation of results easier. There is also a practical reason for using this kind of a priori threshold values: the estimation of the threshold values in the context of e.g. the so-called smooth transition regression models has been found to be notoriously difficult and it is has been found very difficult to judge the accuracy of the estimates.

9 In fact, there is a correspondence even between threshold models and bilinear models (cf. Priesley 1988 for details). 10 Here might refer to Verbrugge (1997, 1998) which shows (using the nonparametric triples test of Randles) that there are clear asymmetric features in many macroeconomic series, most notably in price series. Moreover, the existence of asymmetries varies a lot across countries.





The basic threshold model can be expressed as 

α11yt–1 + α12xt + u1t, if st < s0 and

 

α21yt–1 + α22xt + u2t, if st ≥ s0

(2.9)

with Var(ui) = δ2i. Here s is the transition (threshold) variable and s0 the (fixed) threshold value. The transition variable is here indexed for period t but obviously it can have a lag.11 Usually, the lag length is also unknown which obviously complicates the problem. In our exercise the lag length is always zero. Statisticians very often use a version (2.9) which only includes autoregressive terms on the right-hand side. By definition they are Threshold Autoregressive (TAR) models. In these models, the transition variable is in most cases some lagged value of y. For obvious reasons, the emphasis in TAR models is the dynamics of the y variable. These models are quite popular in ecological applications but not so much in economics except for applications in finance, of course, where univariate models could be considered on a priori grounds, or in error-correction processes (Enders and Siklos, 2001). Within the set of threshold autoregressive models, we may distinguish a set of momentum threshold autoregressive models (M-TAR) in which the transition variable is the difference of the dependent variable. With such models (see, e.g. Enders and Granger (1998)), we may test whether the time series are more persistent with positive growth and vice versa. Enders and Granger’s (1998) application with interest rates gives, in fact, strong support to this notion suggesting, more precisely, that interest rates respond to the deviation from the equilibrium level only when the discrepancy is positive. It is easy to find a straightforward application to a threshold model from economics. Take for instance a simple model inflation where inflation is positive with positive values of ouput gap (or unemployment below the nature rate of unemployment) but zero with negative values (Figure 2.1). Then the output would be the transition variable and zero the corresponding threshold value. Obviously, there could be leads or lags in the transition variable and nonzero threshold values but the main concern would be a difference in the relationship between inflation and output over the cycle. When using specification (2.8) we assume that all parameters and the error term are different for these two regimes (obviously the number of regimes can be more than two although we mostly deal with the 11

This is not to say that dynamic adjustment towards equilibrium is unimportant. Results in Holmes and Maghrebi (2005), for example, suggest that, with interest rates at least, the speed of adjustment is larger with increasing rates.



yt = 

Estimation and Aggregation Concerns 19 Figure 2.1

Illustration of a simple threshold model with cyclical data

3 gap inflation

2

1

0 11

21

31

41

51

–2

two-regime case). In other words the whole data generating process is different. It could, however, be that the regimes are not that much different and only part of the parameters vary over regimes. Thus, we could arrive at the following simple case: yt = α1yt–1 + α21x–t + α22x+t + u1t , where xt = x–t if st < s0 and xt = x+t if st ≥ s0

(2.10)

In this specification, nonlinearity would only apply to the slope of x, not the dynamics of the model (here the lagged y term) or the error term. In other words, the stochastic structure would be the same for the both regimes (in particular, the variance of the error term u would be invariant over regimes and hence δ21= δ22).12 Equally well, we could imagine that nonlinearity would apply to the ‘speed of adjustment’, in (2.4), the α1 parameter.13 Accordingly, if we had an error-correction model, the coefficient of the error-correction term would be a possible candidate for regimes shifts. In an error-correction set-up, we would, in fact, 12

A common alternative to (2.10) is to estimate yt = a1yt + a2xt + a3Dxt + u1t , where D = 1 when st ≥ s0 and 0 otherwise. 13 There is also an extensive literature of cases where the condition variance of the error terms is subject to nonlinear dynamics in the ARCH/GARCH framework. Here focus, however, entirely on coefficients of the behavioural/mean) equations.



–1


have several alternative ways of expressing the regime-sensitiveness: both the long-run coefficients and the short-term impact effects plus the speed of adjustment parameter(s). In specifying the model, we have not, in general, used the error-correction structure.14 Hence, the choice of nonlinear elements in the estimating equations is more straightforward. In a typical case, with the Phillips curve, we focus on the output variable, i.e. the impact of output (or, real marginal costs) on current inflation. This is not say that we firmly believe that the past of future (expected) inflation only affects in a linear way. Rather it is the case that testing all features of nonlinear elements in the basic equations is beyond the scope of our discussion. Although we mainly deal with the two-regime case, more complicated specifications may make sense. In more concrete terms, we have found some evidence on a corridor type adjustment, especially in the case of policy (Taylor) rules. Thus the model would be of the following general structure:

(2.11)

It could be, for instance, that α22 is zero because small deviations from target values of interest rates do not lead to any policy reactions. Only if the values of s deviate ‘enough’ could such a reaction occur. In the case of policy reaction functions such behaviour could be rationalised by e.g. measurement errors: if the policy-maker knows that the transition variable (say, inflation) includes some measurement error, immediate reactions to small deviations from the target value would not be optimal policy. Alternatively, one could defend the ‘corridor’ specification by adjustment costs. If changing interest rates (in the case of a Taylor rule) required some discrete costs, policy reaction would be delayed until the deviation from the target value is large enough. There could also be some ‘political economy’ or ‘psychological’ reasons. One very interesting feature of threshold (autoregressive) models is that they can give rise to limit-cycle behaviour. In this respect they come close to features of nonlinear differential equations. Thus, if we switch off the stochastic term the threshold autoregressive model may have a solution that has an asymptotic periodic form. 14

In the literature, two classical (Canadian) datasets have been used quite extensively, one dealing with lynx and the other with mink and muskrat. In these examples, the main issue was the dynamics of these animal populations, which showed quite complicated asymmetric lag structures. See Tong (1983) for details. For more recent ‘economic’ applications see Kavler et al. (2008) and the references cited therein.



 α11yt–1 + α12xt + u1t, if st < rL  yt =  α21yt–1 + α22xt + u2t, if sL ≤ st ≤ sU   α31yt–1 + α32xt + u3t, if st ≥ sU.


Although the basic threshold model is intuitively appealing it has some questionable properties. Above all, one may find it difficult to accept the idea that the regime change represents a discrete jump. A small change in the transition variable (in the neighbourhood of the threshold value) may change all parameters completely. An obvious remedy is to introduce some smoothness into the transition of the parameters. That is easy with the simplified version of the threshold model (where the threshold only affects some of the parameters). Thus, we could arrive at the following specification: yt = α1yt–1 + α2xt + [α3 + α4G(s)]xt + ut

(2.12)

where the indicator function G would be defined for the interval {0, 1}. In practice, G could be either an exponential or a logistic function. In the latter case, which is more frequently used later on, we may write G(r) in the following way: G = 1/{1 + exp[–γ(st – s0)]}

where γ is the smoothness parameter which defines the shape of the threshold. By setting the parameter very high we end up with a switching regression (threshold) model as can be seen from Figure 2.2. By contrast, if γ equals to zero, G = 1⁄2, and we end with a linear regression model. Figure 2.2

A comparison of a rigid and smooth threshold G 1

0.5 γ=1 γ = 10

1

St



(2.13)


If we have more than one threshold (the ‘corridor’) we can simply generalise G accordingly. Thus, in the case of two thresholds, we may express the logistic G in the following way: G = 1/{1 + exp[–γ(st – s0)(s – s1)]}

With a sufficiently large value of the smoothness parameter we can derive a corridor type nonlinearity for the effect of x on y (see Figure 2.3 for an illustration). The figure is drawn in such a way that the threshold effect is not symmetric: there is a ‘corridor’ but the effect of x on y differs depending on which side of the ‘corridor’ we are. We could imagine that, for instance, the central bank does not react to small deviations from the inflation target but it reacts to high inflation and low inflation (deflation) differently (for more details, see e.g. Granger and Teräsvirta (1993)). Estimation of the smooth transition regression model is in principle straightforward: it can be done with maximum likelihood or nonlinear least squares (which here represent suitable quasi-maximun likelihood estimation). It is only that some computational problems may arise, in particular with the smoothness parameter γ in the context of smooth transition models. One may also find some problems in testing the sequence of thresholds but may bypass these things and only refer to Figure 2.3

An effect of smoothing on a two threshold model G 1

0.5 γ=1 γ = 10

–1

1


St


(2.14)


Granger and Teräsvirta (1993), Hansen (1999a, b, 2000) and Luukkonen et al. (1988)). In the case of switching regression threshold (2.10) testing is reasonably straightforward because we can just test the parameter restriction α21 = α22 with the conventional Wald test. With smooth transition models things are more complicated because if γ = 0, the linear model (H0) is not identified. Hence, LR, Wald and LM tests cannot be used. One way to circumvent the problem is to linearise the transition function G which in the logistic case gives us the following testing equation: (2.15)

so that the test boils down in testing the parameter restriction β3 = 0 (cf. Luukkonen et al., 1998). When estimating the model, we face the additional problems of a panel data setting which is the typical environment for estimating the reported equations. Estimating dynamic equations from panel data is not the easiest exercise in econometrics but when we add nonlinearity things can be quite complicated. We discuss these problems more in the context of reporting the results but here a couple of most compelling problems should be mentioned, at least. When using the threshold model, we typically have to assume that the threshold parameter is constant over time and over countries or regions. The later assumption is not, of course, completely innocent. Countries might well differ in terms of the explicit inflation target, or the critical value of the state of product markets. In countries where markets function ‘well’, wage or price stickiness could be of less importance and hence inflation or employment reactions show larger ‘tolerance’, which in turn would show up in larger values of output gap, for instance. Basically, the problem can be solved by using individual country data, and so is in fact done in the subsequent analysis. Another weak point is the dynamics of the model that basically boils down in the speed of adjustment parameter. We have good reasons to believe that these parameters differ across countries and estimating equations from single country data confirms this presumption. Still, most of the reported results represent specifications where the dynamics is assumed to be similar across countries. This is motivated by our desire to concentrate on the short-run effects. Only when we find really substantial differences in speeds of adjustment we allow these parameters to vary over the cross-sections (countries). Even now, the conventional way of estimating above-mentioned linear equations (2.9)–(2.11) is to use least squares with fixed cross-section



yt = β0 + β1yt–1 + β2xt + β3stxt + u1t

effects. Efficiency can be increased by using the Generalised Least Squares (GLS) (to account for different error variance) and the Seemingly Unrelated Regression (SUR) estimator to account for correlated error terms. There are, however, several obvious problems in estimating the models. First of all, the data may not be completely stationary. More serious problems arise however, because of the dynamics. Most of the models include a lagged dependent variable (or an error-correction term, see the Okun curve) which, in the panel setting, makes least squares a rather poor estimator. An obvious choice is to move the use of the Generalised Method of Moments (GMM) estimator which in practice means using the Arellano-Bond estimator. Although, that ought to be the right thing we know that the estimator is often very sensitive to instruments and other features of estimation. Thus, in this way, we have to look at a wide range of estimates with different estimators and, as usual, with different data samples and variable measures. With these kinds of data, one cannot disregard the endogeneity problem, either. Take for instance the Phillips curve and the Okun curve. In a sense, all variables are endogenous and, moreover, the expected future values are also random (unless we have some survey data for them). In an IS curve, interest rates drive output and in the Taylor rule, output determines the interest rate. In this kind of setting, least squares seem a quite poor alternative but it is still used as a basic alternative. The reason is simple: we need some benchmark values that are reasonably robust and computationally well-behaving for various comparisons, especially when we move to nonlinear specifications. As pointed out earlier, nonlinear threshold models are not the way of allowing a nonlinear structure in the particular transmission mechanism. Earlier, in the 1970s and 1980s, a lot of work was done using socalled disequilibrium models which took the form: q 1 = a 0 + a 1P + a 2Y + u 1 q 2 = b 0 + b 1P + b 2X + u 2 q = min(q1, q2)

(2.16)

where superscripts denote possible regimes which could, for instance, be interpreted as excess demand and excess supply regimes so that the realised values were determined on the basis of the minimum condition (see e.g. Quandt (1988)). The identification of regimes is obviously more difficult than in the case of threshold models because the choice of regimes cannot be based on any observable data. The minimum condition (‘short-side rule’) provides the necessary information for each regime but still there are serious difficulties in identification of





regimes. The minimum condition in (2.16) will fail if there are some frictions in the market and the markets are not efficient. Then, we have to adopt a less stringent condition q < min(q1,q2) which is obviously less informative in identification of regimes because we do not know how far actual transactions are from intersection of (notational) demand and supply curves. This (so-called Muelbauer-Hajivassiliou) condition with a conventional supply-demand model is illustrated in Figure 2.4. In addition to these conceptual issues, it is probably fair to say that the disequilibrium models (of type equation (2.16)) have been notoriously sensitive to the estimation procedure (cf. e.g. Stenius and Virén, 1984). Moreover, the disequilibrium models have been notoriously sensitive to the estimation procedure. In our case, this kind of disequilibrium specification is not very attractive because in general we do have some idea how the regimes are Illustration of the minimum conditions

S

E(Q)

D

Q The left-hand side of the supply and demand axes stand for the fixed minimum condition (bold lines) while E(Q) illustrates the Muelbauer-Hajivassiliou minimum condition for heterogeneous markets.



Figure 2.4


M=

p11 1–p11 1–p22

(2.17)

p22

Denote the path of the (unobserved) two-state first order Markov process that would take values 0 or 1 by st and, accordingly the observed time series that depends on st, by xt. Then the density of xt conditional to st would be: F(xt|st, β) = (2πσ)–1⁄2 exp[–(xt – us)2/2σ2]. On other words xt would be uncorrelated white noise with switching mean. The states could be characterised as ‘high’ and ‘low’ which would be useful in characterising e.g. business cycle fluctuations and inflation dynamics. If we were just testing the existence of regime changes, this approach would be appealing. We do, however, want to show that regime changes appear to follow some specific pattern that can be easily traced from observable variables (most often from the cyclical situation). That is why we here concentrate on the threshold models. Even so, it is worth pointing out that the menu of ‘traditional’ nonlinear models is quite rich. Take for instance, the Brechling’s (1973) estimating equation which he used in testing the so-called nonlinear aggregation hypothesis. This equation was derived from the following model: wt = bg(Ut) + 2bg″(Ut)Var(Ut) + k0pt + a′C(wt – k0pet)

(2.18)

where g corresponds to some nonlinear function (of the unemployment rate) Var(U) being the variance of cross-sectional unemployment



related to observable indicators (transition variable) so that identification of regimes can be done with the help of those data. Moreover, in our case the regimes usually differ from each other only in terms of the parameter values – not in terms of the variables or the functional form. Needless to say, in such a case the minimum condition may not make sense at all. Thus, rather than the disequilibrium model, we could used other econometric tools like the state-space models (see Hamilton (2008) for general reference). In Hamilton’s regime-switching models the big difference vis-à-vis the threshold models is the fact that the regime switches occur with latent (unobserved) states and not with observed states as in the threshold models. In the model, time-series dynamics are governed by a finite-dimensional parameter vector that switches depending on which of the two unobservable states is realised with the state of transitions governed by a first-order Markov process. Thus, in the simplest case, we would have a transition probability matrix which could be of the following form:

rates. b, k, a and C reflect the underlying microparameters. Brechling used various nonlinear functions (such as log(U) or Uθ) in deriving the final estimating form. The problem with this kind of functional forms is the fact that while they allow for testing linearity they do not really provide much help in economic interpretation of the results. Moreover, they typically lead to quite complex forms which create their own computational problems. Some of the nonlinear structures may be difficult to express with explicit functional forms. Take for instance the plucking model which was originally introduced by Friedman (1993). Friedman proposed a model of business fluctuations in which output cannot exceed a ceiling level, but will, from time to time, be plucked downward by recession. The model implied that business fluctuations are asymmetric, that recessions have only a temporary effect on output and that recessions are duration dependent while expansions are not. This behaviour is illustrated in Figure 2.5 below. This model could perhaps be expressed by means of the minimum condition of the type q = min(capacity, Q(t)) where Q represent the level of output obtainable in the absence of any capacity constraints (as a consequence of, say, demand disturbances). Even so, it is not

Figure 2.5

Friedman’s plucking model

REAL OUTPUT

BUST

BUST

BOOM (RECOVERY)

BOOM (RECOVERY) BOOM (RECOVERY) BUST





immediately clear how this can be transformed to an estimating (and testing) equation. Instead one may simply test whether the cyclical movements of, say, GDP are symmetric by using various time-series testing procedures. Here we may refer to Diebold and Rudebusch (1990) who examine the basic business cycle summary statistics and find that with historical data Figure 2.6

Kernel densities of output growth with the EU27 data for 1990–2008

2.0 G*(G0)

Dens ity

0.16

0.12

0.08

0.04

0.00 –2

0

2

4

6

8

10

12

14

16


18


De nsity

1.5

from the mid-1800s to the 1930s the mean duration of expansions was 31.5 months and mean duration of contractions 23.5 months. For the post-war data, the differences were even much larger. Thus with the US, the mean duration of expansions were 49.9 months and the duration of contractions 10.7 months only. This suggests that output growth may indeed follow some nonlinear pattern which would be roughly consistent with the plucking model. A look at the more recent EU data suggests that the distributional properties of positive and negative output growth values are indeed very different which again suggests that they be generated by different data generating mechanism (Figure 2.6). Assume for a while that output growth followed the plucking model. This would have powerful implications in terms of policy. Policy effectiveness would crucially depend on the state of economy vis-à-vis the capacity constraint. If the economy were close the constraint expansionary policy would be very much inflationary. Only if the timing of policy would just match the cyclical downturn might one alleviate the severity of eventual depression. As for the Phillips curve, it is hard to see that it could be linear in terms of the observable levels of output. Even if output were to grow very slowly there could be strong demand pressures and high rate of price and wage inflation. On the contrary, high growth rates of output would not necessary lead to immediate inflation pressures. As for a decrease of output, immediate price and wage effect might not materialise because of lagged demand pressures and wage and price stickiness. Obviously, the plucking model would change the interpretation of output data from the point of view of excess demand. Now low growth numbers of output follow either from low demand growth or from binding capacity constraints. The implications in terms of price pressures would be different indeed even if the Phillips curves were completely linear. If the underlying Phillips curve were nonlinear even some perverse results could emerge. It is very difficult to find remedy for this problem; perhaps the only thing that can be done is to use alternative proxies for cyclical situation (output growth, output gap, the unemployment rate and the deviation of the unemployment rate from the natural rate).

2.3

Aggregation

One issue which is highly relevant in the context of the EU is aggregation. Basically, the question is: does it make a difference if we consider the data for each individual country or just look at the average





values of the whole euro area. From the point of view of economic theory, we know that aggregation matters. The case of a nonlinear Phillips curve (Figure 2.7) is usually presented as an illustration of this effect: with heterogeneous labour markets, an uneven distribution of unemployment leads to higher equilibrium unemployment than with well-functioning labour markets where unemployment rates are more or less equal (this is the basic idea in the so-called nonlinear aggregation hypothesis developed by Brechling (1973) which is discussed in more detail in Chapter 5).15 But Figure 2.7

Implications of the convexity of the Phillips curve

1

α

A

E(u) 0

u

u1 u2

u*

–1

B ∏

Note: The figure reflects the convexity of the Phillips curve ππ′. Assuming that the Phillips curve is of the form π = f(u – u*) + µ we may use Jensen’s equality in deriving the properties of zero-inflation equilibrium of the system. That is, E{f(u – u*) ≥ f[E(u – u*)]}; now knowing that f(u – u*) is decreasing with respect to u – u*, we can show that when E(π) = 0 so that E[f(u – u*)] = 0 (E(µ) = 0 by definition), E(u) – u* ≥ 0.

15

The nonlinear aggregation hypothesis basically originates from Lipsey’s (1960) dispersion hypothesis.



∏

what are implications in terms of empirical analysis? It turns out that aggregation is of nontrivial importance both in terms of estimation and interpretation of the estimated results. In the European context, aggregation is surely an issue: we have common monetary policy which is based on aggregate (or average) values of the euro area variables but other policies mainly belong to national governments. Moreover, countries seem to differ a lot in terms of their level of income, structure of production and economic policy record (for details, see section 2.4). In this environment, there is an obvious conflict of interest because the governments of individual countries evaluate the operations of the Central Bank from their own perspective and it may well look that the policy pursued is not consistent with the economic environment of some individual country. The common monetary policy does not only manifest itself in ‘common’ monetary policy decisions but also in the way the whole policy analysis is carried out. A concrete example of this is the macro model for the euro area economy, more specifically the Area-Wide Model of the ECB (see Christoffel et al., 2008) which only uses the data from euro area aggregates or averages. Thus, cross-country differences, or dispersion in the relevant variables, do not matter.16 At least implicitly, this is based on assumption that the basic features of member countries are sufficiently similar that policy transmission can be treated as being roughly the same for all countries – from the perspective of modelling. Obviously countries and regions are different but the differences are not equally important. The fact that individual coefficients and elasticities are different obviously leads to severe estimation and interpretation problems. Take for instance the case illustrated in Figure 2.8. If countries differ a lot in terms of basic parameters it would be difficult to reach any affirmative conclusion of the nature of the eventual relationships. Moreover, the estimation results may be quite sensitive to details of the estimation procedure. Haque et al. (1999) shows that the conventional fixed effects model may produce highly misleading

16

It is obviously a complex matter to decide upon the extent to which the Governing Council of the ECB takes decisions based on aggregate rather than disaggregate information. The key issue here however is that some form of aggregation whether before or after estimation needs to take place and in practice substantial use is made of aggregated information.




32 Asymmetry and Aggregation in the EU Figure 2.8

An effect of heterogeneity of estimation results

Y

country 1

country 3

X

results. In particular, the highly significant but nonlinear effects can be detected for some variables even though the basic structure is perfectly linear.17 Obviously, there is a simple remedy to this problem: that is a scrutiny of the results for individual micro units (countries). If results with the individual country data differ substantially from the pooled cross-country data (with common coefficients) one has to reconsider the estimation procedure with the pooled data. In what follows in this book, we do indeed estimate all specifications with individual country data even though most results are presented in the form of pooled cross-country data. Aggregation is also an important issue in the context of markets in disequilibrium. Then if we have some frictions which prevent markets from

17

The heterogeneity problem becomes serious if the slope parameter(s) are not fixed by somehow systematically relating them to background variables (see Haque et al. (1999) for details). Not surprisingly, the problems become more serious with dynamic (panel) models.



country 2

clearing and, at the same time, prevent free movement of resources from market to market (i.e. markets are no more efficient), we end up with the case where, at the same time, we have (sub) markets with excess demand and excess supply (Figure 2.4). That in turn implies that transactions will be smaller than what the aggregated supply and demand curve would suggest. Again, we would have problems in identifying the true (nonlinear) data generating mechanism if we just had aggregate data (see Quandt, 1988 for more details). But things would be at least equally complicated when the underlying micro relationships were nonlinear. Then, even simple aggregation of data would produce great problems in identification of the true data generating process. This is shown by Granger in several papers (cf. e.g. Granger (1987, 1988)).18 Not only would nonlinearity make it difficult to assess the impact of different (policy) variables on the aggregate level but it would also make the interpretation of data generating mechanism very tedious. If the policy-makers use only aggregated data they could get the false impression that the data generating mechanism is linear even if the opposite would indeed be the truth. Hence, a crucial question is whether aggregation itself affects the properties of the data. If for instance, the country data generating processes were indeed nonlinear would this property disappear, or increase, or stay the same as we aggregate over countries? Obviously we could arrive at nonlinearities at the aggregate level even if the underlying models at the country level were linear if the random shocks were asymmetric (in a suitable way). This is an issue from which we know simply too little to make even rough conclusions (see however, Folk et al. (2004) and Jones et al. (1996)). Granger and Lee (1993, 1999) present some Monte Carlo evidence on impact on aggregation appearance of nonlinearity. They use some conventional nonlinear models to generate the data and apply several tests (the Neural Network Test, the Tsay test, the RESET test and the Dynamic Information Matrix test) to detect nonlinearities from series that had been aggregated in different ways. In general, the tests could detect nonlinearities only in a fraction

18

Granger (1988) shows that if the data were generated by an ARIMA model family (and the random terms were at most correlated contemporaneously) the aggregated series would still have the same structure. If there were some common factors, identification of micro/macro structures would become very difficult (even impossible) but the most severe problem would occur when the micro relationships were nonlinear. The properties of the aggregate data would grossly deviate from the micro data.





of (aggregated) series which obviously can reflect the low power of the tests or the fact that aggregation indeed destroys the nonlinear features of the data generating mechanisms. A similar result emerges in the case of temporal aggregation. Moreover, with temporal aggregation the loss of power seems to increase with the extent of aggregation. To investigate the importance of this problem, we have carried out some Monte Carlo simulations where the work-horse is the following simple threshold model: yt = ayt–1 + b1xtxt–1≤0 + b2xtxt–1>0 + ut

(2.19)

where u is a white noise stochastic term that is cross-sectionally either uncorrelated or correlated. The threshold model is, of course, a rather specific alternative of nonlinear models but we know that it provides a Figure 2.9

Kalman filter estimates of threshold model coefficients

coefficient

0.5 0.4 0.3 0.2 0.1 0.0 –0.1 –0.2 –3

–2

–1

0

1

2

3

individual x 8

coefficient

4 0 –4 –8 –12 –16 –0.8 –0.6 –0.4 –0.2 0.0 0.2 average x

0.4

0.6

The data are simulated with sample size of 100.



0.6

reasonable good approximation of different nonlinear models. To test nonlinearity, we have used both the BDS test and a Wald test to test whether a threshold model structure can still be detected in the aggregated time series. The number of cross-sections has been either 15 or 27 reflecting the number of countries in the euro area (or the ‘old’ EU) or the countries of the ‘current’ EU. A summary of findings is reported in Tables 2.1 and 2.2 and Figure 2.10. Figure 2.9 illustrates the effects of aggregation in a way a bit similar to that which Priestley (1998) uses for the identification of parameters of state dependent processes. This involves generating data with model (2.19) using parameters on row 1 in Table 2.1 and estimating a simple Kalman Filter model of the type yt = a0yt–1 + atxt + e1t, where at = at–1+ e2t and graphing the (smoothed) estimates of a against variable xt–1 with a single micro (country) data series and with aggregated data from 27 micro units. Quite clearly, the nonlinear structure can be detected from the micro data but no longer from the aggregated data (here we do not need the results for the original micro data – in practically all cases the hypothesis of linearity (= equal coefficients) is rejected). With the aggregated data it looks like there is no relationship between y and x. In Table 2.1, the numbers displayed are percentage rejection frequencies of a hypothesis c1 = c2 which implies a linear model (symmetric behaviour above and below the threshold). In the Table, the 6th model is a linear model (so, the data are produced with a linear model and a nonlinear threshold model is estimated from the corresponding aggregated data). The results suggest that, if the number of micro (cross-section) units is 15, aggregation does not completely remove nonlinear structure form the Table 2.1

LR test results for a threshold model

Simulated model 1: c0 = 0.15, c1 = –0.10, c2 = 0.50 2: c0 = 0.15, c1 = –0.10, c2 = 0.10 3: c0 = 0.15, c1 = –0.50, c2 = 0.50 4: c0 = 0.50, c1 = –0.50, c2 = 0.50 5: c0 = 0.50, c1 = –0.10, c2 = 0.50 6: c0 = 0.15, c1 = 1.00, c2 = 1.00

n = 15, 5%

n = 15, 1%

n = 27, 5%

n = 27, 1%

43.3 36.2 37.0 41.0 35.8 7.0

29.8 25.0 22.6 28.4 24.2 2.8

28.0 22.0 21.4 22.6 24.2 4.6

17.6 12.4 15.0 12.6 15.2 1.6

The data (for different ‘countries’) were generated with a threshold model yt = c0yt–1 + c1(xt | xt–10) + u′t, and test the hypothesis c2 = 0. Simulation results with these two alternatives were practically identical.20 The computed BDS tests point in the same direction. That is, if the tests are computed for the aggregated time series, the 5 per cent critical value of BDS(2) test statistics is exceeded in roughly 10 per cent of cases (e.g., in 8.8 per cent of the cases with model 1 and n = 27). Thus, it seems that a nonlinear structure that is generated by a threshold model cannot be captured any more after aggregation of micro units. To get some more flavour of the simulation results, a set of Kernel densities of the coefficient estimates of the threshold models are displayed in Figure 2.10 (abbreviation c1-1-15 denotes coefficient c1 with model 1 in Table 2.1 and with 15 cross-section units in aggregation). Similarly, the coefficients from a linear model (model 6 in Table 2.1) are shown on the right-hand side of Figure 2.10. We see that the estimated coefficients c1 and c2 in Figure 2.10 deviate a lot from the values that are used in the data generating mechanism. The general tendency is a convergence towards the average values of the coefficients. Thus, one might assume that if the number of cross-

19

The rejection frequencies with the alternative tests come so close to the LR test that we conclude that the results may not only reflect the particular LR test procedure used compiling Table 2.1. 20 By contrast, the results tuned out to be quite sensitive in terms of correlation of the random terms of the micro units in the same way as in Granger and Lee (1993). Also if we assume that the aggregate data are not a simple sum of the micro units but there is some additional random term (say, measurement error) the test results change a lot, i.e. the rejection frequencies fall. If for instance with model 1 in Table 2.1, the variance of the random term is the same as the variance sum of micro units, the rejection on frequencies fall to one-third of those in Table 2.1 (to 15.6 and 7.0 per cent respectively).




Estimation and Aggregation Concerns 37 Figure 2.10

Distribution of coefficients c1_LIN_15

c1_1_15 10

0.8

Density

Density

8 6

0.6

0.4

4 0.2

2 0 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

0.0 –2

–1

c2_1_15

0

1

2

3

4

c2_LIN_15

12

0.8

8

Density

Density

10

6

0.6

0.4

4 2

0.0 –2

–1

0

1

2

3

2

3

2

3

c1_1_27 10

c2_lin_27 0.7 0.6 Density

Density

8 6 4

0.5 0.4 0.3

2

0.2 0.1

0 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35

0.0 –2

c2_1_27

–1

10

0

1

c2_lin_27 0.8

6 4

Density

Density

8 0.6 0.4

2 0.2 0 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40

0.0 –2

–1

0

1

Note: On the left-hand side of the figure the graphs are Kernel densities for parameters c1 and c2 for model 1 shown in Table 2.1, the number of micro-units (countries) being either 15 or 27. The right-hand side shows comparable densities for the same two parameters in a linear model, labelled 6 in the table.



0.2

0 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40


Table 2.2

Mean values of ˆc1 and ˆc2 from the simulated data

Model 1: c0 = 0.15, c1 = –0.10, c2 = 0.50 2: c0 = 0.15, c1 = –0.10, c2 = 0.10 3: c0 = 0.15, c1 = –0.50, c2 = 0.50 4: c0 = 0.50, c1 = –0.50, c2 = 0.50 5: c0 = 0.50, c1 = –0.10, c2 = 0.50 6: c0 = 0.15, c1 = 1.00, c2 = 1.00

n = 15, ˆc1

n = 15, ˆc2

n = 27, ˆc1

n = 27, ˆc2

0.153 –0.015 –0.089 –0.079 0.154 1.001

0.248 0.017 0.085 0.086 0.249 0.960

0.161 –0.013 –0.064 –0.060 0.164 0.969

0.239 0.011 0.055 0.058 0.237 0.978

21

This seems indeed to be the case. Thus, for instance with model 3, the rejection frequencies of the linear model go down to 9.2 per cent (with the 5 per cent test size) and 5.6 per cent (with the 1 per cent test size) when the number of cross-sections is increased to 100 (notice that in model 3, the comparative values for n = 27 are 21.4. and 15.0). As for the ‘other end’, it seems that effect of aggregation becomes apparent quite quickly when number of micro units is increased from 1 or 2. For instance with simulation model 1 in Table 2.1, the rejection rate with 5 per cent level is 0.90 when n = 3 but when the number of micro units is increased to 5, it falls to 0.63 per cent. 22 The analysis of Granger and Lee (1999) is much more extensive in the sense that they scrutinise a wide range of nonlinear models although some of the nonlinear data generating mechanisms are not very realistic from an economic point of view. Moreover, they just test the generated times series with various nonlinearity tests and do not attempt to discover the underlying nature of nonlinearities.



sections increases the aggregate data no longer show any signs of nonlinearities.21 Notice that the threshold model performs reasonably well in the sense that it does not find thresholds from the data that has been constructed by a linear model which also show up in the fact that the coefficients c1 and c2 turn out to be practically identical (Figure 2.10). The mean values of cˆ1 and cˆ2 are reported in Table 2.2. Although the effect of aggregation on the series which have been generated by a threshold model seems quite clear we have to acknowledge that this result may not be generalised to other nonlinear models. Thus, for instance, if the data were generated by quadratic polynomials the power of different test procedures with the aggregate data can be quite different. However, the results of Granger and Lee (1999) provide some guidance in this matter. To illustrate the results of Granger and Lee (1999), we might here reproduce some of their main findings for 20 cross-sections and a sample size of 200 (cf. Table 2.3).22 In this example, the data were derived using a

Estimation and Aggregation Concerns 39 Table 2.3

Summary of Granger and Lee findings

Type of test

Neural Network Test Tsay test Dynamic Information Matrix Test Reset Test

No aggregation

Uncorrelated errors

Equal variance

Highly correlated errors

780 52

61 71

187 56

501 68

46 59

46 70

71 58

85 76

simple threshold model of type (2.19). In the case of ‘no aggregation’ the data are thus reflecting this nonlinear data generating process. The Granger and Lee findings are somewhat puzzling. Only in the case of Neural Network tests, is there a clear difference between the original series and the aggregated series (in the case of the original series, the first column just shows the power of the test in detecting the nonlinear structure in the data). The other three tests are not helpful at all: they do not detect nonlinearity in the original series and they do not discriminate between the original and aggregated time series. The performance of tests is somewhat better in terms of other nonlinear alternatives (like the bilinear model) but even then it appears that detecting the (right sort of) nonlinearity is not a settled issue.23 The problem is, of course, the fact that we do not have analytical results of the size of the aggregation effect. Granger (1987) and Granger and Lee (1999) give some examples of the implications of aggregation in the case of nonlinear time-series structures but for obvious reasons, we cannot produce some general results. That is why we also experiment with some alternative nonlinear threshold model structures. In practice, this boils down in using different coefficient vectors for equation (2.19). One has to acknowledge that aggregation does not only affect the interpretation of the aggregate data but also the possibilities of getting information from important micro features of the data. Putting it very simply, if the data for individual countries mainly reflect country 23 Obviously, the results may reflect some specific features of the model and testing procedure. Thus, for instance, Granger and Lee (1999) use very different parameter values for the two regimes.



Numbers are frequencies of rejection at the 5% level out of 1000 replications. The cross-section error-terms are derived as follows: uit = εt + ηit, where the variances of ε and η range from 0 to 1. With uncorrelated errors εt = 0, while with highly correlated errors, Var(ε) = 0.9 and Var(η) = 0.1.

specific features, aggregate data may be quite impotent in reflecting these features. This point is nicely illustrated by Granger (1987) with the following simple model. Assume that the country relationship are of the form: yit = axit + b(xit)2 + εit where xit = µt + x′it where µ is the common factor (mean) for all N countries and x′ the individual country dispersion (with zero mean). For our purposes, we could assume, for instance, that y is inflation and x output gap. To simplify notation, write S(y) = ∑yi and S(x) = Nµ + ∑x′i which reduces to Nµ with large N. Now Granger (1987) shows that if µ ≠ 0, i.e. there is a common factor in the data, S(y) = aS(x) + b[S(x)]2 + S(ε), the original nonlinear form is maintained at the macro level. But by contrast, if µ = 0, so that there is no common factor, S(y) = aS(x) + bS(x2) + S(ε). Notice that now the squared term reflects the variance of the micro (individual country) terms which are not readily observable. Moreover, S(x2) is probably not related so [S(x)]2.24 Thus, the ‘true’ nonlinear relationship cannot be estimated from the aggregate data unless the common factors are dominant enough. Quite clearly, one has to be careful in interpreting the properties of the aggregate EU/euro area series from the viewpoint of individual countries. Aggregate data may simply hide important features of the underling micro series and, moreover, the connection between micro and macro may be quite weak. This fact is illustrated again with the EU27 data; see Figure 2.11. As one can see, cross-country differences in output growth do not nicely coincide with the aggregate output growth, rather the relationship is almost nonexistent. Thus, the aggregate data may not reveal nonlinear features that are incorporated in individual country data. Here we have focused on cross-sectional aggregation only and it is certainly one of the main concerns from the policy point of view. Even so, temporal aggregation also matters (as shown by Granger and Lee (1993)). In practice, we have only annual data on some fiscal variables which effectively hide possible short-term nonlinear features of the data. Needless to say, we can arrive at a situation where both crosssectional and temporal aggregation affect the data at the same time

24

This can also be seen from the data. Thus, with the EU27 data, correlation between the cross-section variance of GDP growth rates and squared GDP growth rate for the whole EU27 turned out to be 0.04 only (N = 52). For details, see Figure 2.11.




Estimation and Aggregation Concerns 41 Figure 2.11 Comparison of cross-section variance of GDP growth rates and the squared growth rate of EU27 GDP 24 Cross-section variance squared growth rate 20

16

12

8

4

96

97

98

99

00

01

02

03

04

05

06

07

08

The sample period is 1976.1–2008.4, the number of countries is 25 (for data reasons, Romania and Bulgaria are not included).

giving an impression that relationships are perfectly linear even though the opposite is true. What looks simple on an average may be much more complicated at the grass-root level as the recent fiscal problems in the European Union have shown.



0

3

In order to explain macroeconomic behaviour in the euro area we use the simple and very conventional four equation model of the economy, consisting of an IS curve, a Phillips curve, and Okun curve and a monetary policy reaction function that we have employed earlier (Mayes and Virén, 2005) and described in outline in the previous chapter.1 This enables us to explore the overall level of activity, inflation, unemployment and monetary policy in an open economy framework. We do not attempt to look at the determination of the exchangerate or the components of the balance of payments, nor do we consider wealth acquisition. Our model is thus incomplete. However, we are not seeking to substitute for Dynamic Stochastic General Equilibrium (DSGE) or other system approaches on this scale, we simply wish to explore the problems of asymmetry and aggregation and the concerns they pose for macroeconomic policy particularly in the EU, where single policies are implemented for member states that are quite heterogeneous. In this chapter we deal just with the IS curve. As set out in Chapter 2, we explore a conventional IS curve of the form: ∇yt = a0 + a1∇yt –1 + a2∇yt–2 + a3rrt–i + a4ret –j + a5∇y*t –k+ εt ,

(3.1)

where ∇y is the deviation of output y from its Hodrick–Prescott filtered trend, rr is the real three-month interest rate (i.e. the nominal

1 A three equation version, omitting the labour market has received considerable attention – Cho and Moreno (2006). However, in our view, including the labour market is essential, as it is one of the core areas of asymmetry in the macroeconomy, behaving in a manner that is clearly different from the asymmetry in aggregate activity.

42



Aggregate Supply and Demand in an Open Economy

rate of interest r less the annual rate of consumer price inflation p), re the real exchange rate with the US dollar (in logs) and ∇y* the deviation of OECD output from its HP trend (lag lengths i, j and k typically vary from 1 to 3 quarters in estimation). Because the construction of output gaps is a controversial issue, we have also carried out all the empirical analysis using the growth rate of output as well (to be denoted as ∆y). As can be seen from the subsequent graphs, these two measures are far from identical in describing cyclical movements and it is no surprise that the results are somewhat sensitive to the choice of output measure. Fortunately, none of the results is crucially dependent of this choice. The IS curve represents a quite conventional open-economy demand relationship in which the real interest rate and the real exchange rate represent the main ingredients. The open-economy nature of the equation is further re-enforced by the inclusion of foreign export demand variables. In addition, in section 3.5 we include two wealth (shock) variables, the rate of change of real house prices (hp) and the rate of change of real stock prices (sp). Then the estimating equation reads: ∇yt = a0 + a1∇yt–1 + a2∇yt–2 + a3rrt–i + a4ret–j + a5∇y*t–k + a6hpt + a7spt + εt

(3.2)

where lag lengths i and j may be different and may also differ over countries. The basic equation is completely backward-looking but we also estimate the model in a hybrid Euler equation for total output which takes the form: ∇yt = a0 + a1∇yt–1 + a2∇eyt+1 + a3rrt–i + a4ret–j + a5∇y*t–k + a6hpt + a7 spt + εt

(3.3)

where ∇eyt+1 is the expected output gap. While we are persuaded that incorporating forward-looking variables is essential for a clear understanding of economic behaviour, we do not take a stand on this and consider both hybrid and purely backward-looking models as well. Estimation of (3.3) is obviously more complicated because we have to deal with expectations. Then we must either have some direct observations of expectations, say, from survey data, or we have to estimate (3.3) with the joint hypothesis of the process of expectation formation, such as, Rational Expectations, which necessitates



Aggregate Supply and Demand in an Open Economy 43


the use of the Generalised Method of Moments (GMM). Equation (3.2) is useful also in the sense that it allows us to compute what is called a Financial Conditions Index (FCI), as explained in Chapter 8. It is simply the weighted sum of the effects from each of the financial variables in the equation can be derived from (3.2) in the following way: (3.4)

where ^ denotes estimated values. In estimating (3.1), or its different versions, the main emphasis is estimation of the coefficients of the policy variables, rr, or more generally rr, re, hp and sp. The IS curve itself is not of itself our main interest. Thus, our main concern is not so much to test different versions of the IS curve against each other as to see whether the role of the policy variables is robust over different specifications. Obviously, the interpretation of the old-fashioned backward-looking IS curve and the Euler equation are quite different. Thus, with the Euler equation, the coefficient of the interest rate reflects intertemporal substitution rather than the impulse response of the policy rate. In the context of the Euler equation, the interpretation of the real exchange rate and the asset prices is also a bit tedious e.g. because we cannot really distinguish anticipated and unanticipated parts of these variables/shocks.

3.1

Monetary conditions

Our primary concern in this chapter is more with aggregation than asymmetry. As we have just noted, there is a lot of variation in the openness of the EU countries not just generally but to each other and to the euro area. Monetary policy in the euro area is set with regard to future inflation in the area as a whole. While the impact of monetary policy on the real exchange rate and the impact of the real exchange rate on inflation at the aggregate level will be taken into account, the impact on the individual countries will be very different. In Mayes and Virén (2000b) we explored this in some detail using data from before the start of the euro area. Our primary concern was to consider the relative importance of the influence of the real exchange rate and the real interest rate in affecting output, i.e. â3/â4 = λ in terms of equations (3.1) to (3.4). Thus for example if λ = 2, a one percentage point increase in the real



FCIt = (1/â3)(â3rrt–i + â4ret–j + â6hpt + â7spt)

interest rate has the same impact as a 2 per cent increase in the real exchange rate. The weighted pressure of both the real interest rate and the real exchange rate on aggregate demand – weighted by their coefficients as in the FCI shown in (3.4) – is often referred to as a Monetary Conditions Index (MCI). Such indexes are no longer in fashion, as it has become much easier to talk about the range of factors affecting inflation in a coherent manner using a model driven framework over the last decade and such simplified measures are no longer required.2 We obtained estimates from a variety of sources including our own data set (reproduced here as Table 3.1) which suggested that most estimates lay in the range 2–6 – our estimate for the euro area as a whole was 3.5 in the light of the evidence. This is considerably smaller than the run of estimates for the US and Japan, which were of the order of 8–10, yet the openness to trade, as conventionally measured, was much more similar to the euro area. The estimates were quite sensitive and varied considerably both for each individual country and across countries. The estimates in column [4] used data from 1972Q2–1997Q4 but we felt that this was bound to cover a regime change as it was not until 1987 that the Exchange Rate Mechanism (ERM) of the European Monetary System (EMS) became sufficiently robust that it could continue for five years without realignment. One might therefore only regard the period after 1987 as belonging to a single regime.3 The results for this shorter period are shown in column [5]. (Columns [6] to [9] show other specifications, respectively adding a price variable, using the growth in GDP rather than the output gap, SUR (Seemingly Unrelated

2 In the second half of the 1990s both the Bank of Canada and the Reserve Bank of New Zealand used MCIs to help explain the bite that the ‘monetary’ variables policy can affect in the short run are likely to have on inflation. However, the exchange rate in particular is affected by a wide range of factors, in addition to policy and changes in it indicate that the central bank might want to reappraise its forecast and the setting of interest rates. Since central banks only change interest rates at predetermined meeting dates except in emergency, such an indicator was thought helpful to markets in their efforts to anticipate policy moves. There are many pitfalls in using such indices (Eika et al., 1996) particularly since it is only nominal values that are available continuously, deflators only being computed at monthly or even quarterly intervals. 3 We searched empirically for structural breaks in the data; while there was some evidence for an earlier break in 1985 and a problem with the breakdown of the ERM in 1992, 1987 appeared the most satisfactory date.




Regression) estimates instead of Ordinary Least Squares (OLS) and adding a long interest rate (bond rate) as suggested by Kennedy and van Riet (1995) among others. The individual country estimates are relatively poorly determined, see Table 3.2 for the case of the shorter data period, but there is little evidence that the exchange rate was anything other than relatively important. Country differences in the main did not appear to be affected by size or openness. The pooled estimates using the whole of the data in a single regression – on the assumption that the coefficients were the same across all the countries were much better determined, with λ in the range 2 to 3. The λ ratios lie mainly in the range 1 to 8 with the Belgian and Portuguese results being implausibly high. Calculating the weighted average value (GDP weights) for the euro area gives a value of approximately 3.5 which is towards the more open end of the spectrum and more than twice as open as using the ratio of trade to GDP would lead one to expect. We therefore conclude that, even in the euro area, movements in the real exchange rate are an important means of adjustment. The drawback is that neither the timing nor distribution of that impact is expected to be very even. The impact tends to be relatively rapid. Mayes and Virén (2000b) estimate from impulse responses that the bulk of the impact is completed within two years. However the size range of the impact across the member states is roughly a factor of 2. This clearly provides somewhat of a problem for a single monetary policy, especially if the parts of the economy most in need of relative stimulation are those that are least responsive. Nevertheless, there is a tendency to forget that inside national economies both sectors and regions are also affected very differently by changes in both interest and exchange rates and so we address this below. We now, of course have not only the benefit of another 11 years of data but can also see whether the formation of the euro area seems to have had any impact on the relative importance of the two variables. However, other factors have also affected the structure of the international system so we need to adopt a difference in differences approach if we are to have any hope of isolating the impact of the euro area. The results for the IS curve in Tables 3.3 and 3.4 using the whole dataset show quite clearly that our simple model with world output, the real interest rate and the real exchange rate can explain the movements of output over time quite well. As in most of our analysis, the dataset is formed of quarterly data for the EU15 less Luxembourg but plus Norway for




2.9

1.5 8.1

Italy

2.3(3), 4.3(6) 2.3(1), 4(2), 2.3(3), 4.3(5), 2.7(6) 10(2), 4(3), 8.8(5), 7.9(6) 2(1) 3(1), 1.4(6) 6.4(4), 1.7(6) 10(2), 9(3), 39(5), 10.1(6)

1.5(3), 2.5(4), 4.2(6) 3.4(1), 1.5(3), 0.5(4), 2.1(6) 3(2), 4(3), 14.4(4), 5(5), 2.9(6)

3.7(4), 0.8(6)

3(2), 4(3), 6.6(4), 6(5), 4.1(6)

2.5, 4(2), 4(3), 2.6(4), 4.2(5), 2.3(6)

3.3(4) 0.4(6) 1.9(6) 2.5(6) 3(2), 4(3), 3.4(4), 2.1(5), 3.5(6)

[3]

Other3

12.8 –4.6 –2.1 5.2 0.8

17.7

29.7

14.3 60.1 3.6 8.9 19.2

[4]

long

short

2.0

2.3 11.6 0.8 1.2 1.5

7.8

3.6

2.4 60.6 8.3 3.1 2.5

[5]

sample

1.5

2.3 5.6 0.8 1.3 0.3

8.4

2.5

3.1 89.9 9.6 3.2 1.9

[6]

short + prices

1.9

2.8 2.3 0.6 0.7 1.5

2.4

4.9

1.3 6.4 –13.3 3.3 2.5

[7]

short ∆GDP

1.1

1.2 14.8 0.5 1.2 1.1

4.3

3.4

1.2 6.2 14.4 3.4 3.4

[8]

short SURE

–1.7

3.4 3.6 3.7 0.8 0.9

13.1

2.7

14.1 2.2 –4.1 4.3 2.1

[9]

short + bonds

10.1057/9780230304642 - Asymmetry and Aggregation in the EU, David G. Mayes and Matti Virén Copyright material from www.palgraveconnect.com - licensed to ETH Zuerich - PalgraveConnect - 2011-04-21

2

Drawn from Dornbusch et al. (1998) Table 10. Drawn from Banque de France (1996) Table 1. Numbers in parenthesis are drawn from Peeters (1998) Table 5. Peeters also provides estimates from EUROMON: Belgium, 6.7; France, 3.5; Germany, 9.0; Italy, 5.7; Netherlands, 8.1 and UK, 3.0. 3 Drawn from Ericsson et al. (1997) Table 1. Numbers in parenthesis denote sources of estimates as follows: (1) central banks, (2) IMF, (3) OECD, (4) Deutsche Bank, (5) Goldman Sachs and (6) JP Morgan.

1

Ireland Australia Canada Japan New Zealand Norway Switzerland US

6.2 (4.6)

1.3

1.4

Germany

Netherlands Portugal Spain Sweden UK

3.0 (2.0) 4.0 (6.1) 0.1 (1.8) (3.0)

2.1

[2]

[1] (1.5)

NIGEM2

Other estimates Dornb1

Our estimates

Estimates of the ratio of the real interest rate to the real exchange rate effect from Mayes and Virén (2000)

Austria Belgium Denmark Finland France

Table 3.1

47

48 Asymmetry and Aggregation in the EU OLS estimation results for the 1987:1–1997:4 period

Name lags

∇yt–1

oecdt–k

R2 (SEE) DW

λ

Austria 2,2,2

0.729 –0.095 (6.39) (0.74)

–0.021 (0.91)

0.009 (0.65)

0.338 (0.65)

0.660 1.90 (0.58)

2.4

Belgium 4,3,2

1.145 –0.457 (6.60) (2.85)

–0.046 (1.28)

0.001 (0.08)

0.334 (1.55)

0.882 1.81 41.7 (0.40)

Denmark 1,3,1

0.105 (1.02)

–0.152 (1.77)

0.018 (1.51)

0.065 (1.12)

0.261 1.96 (0.84)

8.3

Finland 3,2,2

0.773 –0.158 (5.36) (1.00)

–0.152 (2.36)

0.048 (3.90)

0.406 (0.83)

0.881 1.99 (1.19)

3.1

France 4,2,2

0.960 –0.274 (6.64) (1.56)

–0.069 (2.06)

0.027 (1.99)

0.305 (1.35)

0.871 1.94 (0.44)

2.5

Germany 7.3.2

0.545 (2.73)

0.181 (1.23)

–0.072 (0.87)

0.020 (0.84)

0.123 (0.57)

0.911 1.53 (0.81)

3.6

Ireland 1,3,2

0.970 –0.298 (7.18) (2.63)

–0.056 (1.73)

0.028 (1.56)

0.788 (3.09)

0.867 1.81 (0.72)

2.0

Italy 3,2,1

0.701 (8.88)

–0.095 (1.90)

0.012 (1.43)

0.332 (2.35)

0.767 1.97 (0.50)

7.8

Netherlands 1.077 –0.381 1,2,2 (11.41) (4.72)

–0.037 (1.53)

0.016 (1.76)

0.259 (2.34)

0.824 1.86 (0.39)

2.3

Portugal 3,1,1

0.135 (1.30)

–0.081 (2.87)

0.007 (1.24)

0.747 (3.74)

0.901 1.95 11.6 (0.48)

1.518 –0.595 (15.98) (6.53)

–0.008 (1.16)

0.009 (3.01)

0.115 (1.85)

0.982 1.44 (0.18)

0.8

0.226 (1.70)

–0.065 (5.21)

0.052 (4.05)

0.604 (2.30)

0.809 2.33 (0.77)

1.2

0.981 –0.175 (10.50) (1.86)

–0.033 (1.84)

0.022 (2.96)

0.262 (4.34)

0.950 1.83 (0.40)

1.5

Spain 1,2,1 Sweden 1,2,2 UK 1,1,1

0.447 (3.47)

0.537 (5.21)

∇yt–2

rrt–i

ret–j

The dependent variable ∇y is the output gap constructed by the HP filter. rr is the real interest rate, re the real exchange rate with respect to US dollar. oecd denotes the output gap for OECD GDP. λ is the ratio between interest rate and exchange rate elasticities. The numbers below the country names give the lag length for rr, re, oecd (in this order), respectively. The data are quarterly and cover the period 1987:1–1997:4. For the UK, the US output gap is used instead of the OECD output gap. The German equation includes a level and one period dummies for the unification period (1991:1–1997:4). All estimates are OLS estimates t statistics in parenthesis.

the period from the beginning of 1971 through to the end of 2008. However, there are some changes in the performance of this basic equation over time, as can be seen if we compare estimates for different sub-periods.



Table 3.2

Table 3.3

IS curves (basic specification)

Data

re/100

rs/100

World output

lag

1971–2008 0.002 GAP (1.51)

–0.018 (3.06)

0.388 (12.65)

0.697 (34.61)

1971–2008 0.043 g (4.09)

–0.027 (2.83)

0.354 (11.47)

0.705 (32.05)

1971–2008 0.029 g (3.43)

–0.019 (2.12)

0.345 (13.85)

1999–2008 0.064 g (2.89)

–0.077 (1.49)

1971–2008 0.041 g (3.92)

R2/SEE

DW Method

0.720 0.0079

1.98

OLS

0.047 (2.46)

0.737 0.0122

2.02

OLS

0.719 (40.09)

0.034 (2.29)

0.737 0.122

2.05

GLS

0.680 (8.61)

0.494 (7.21)

0.058 (0.32)

0.741 0.0105

2.07

OLS

–0.027 (2.80)

0.369 (12.67)

0.717 (35.42)

0.750 0.0123

1.93

OLS

1999–2008 0.056 g (3.90)

–0.084 (1.53)

0.734 (90.71)

0.483 (7.41)

0.736 0.0105

2.09

OLS

1971–2008 0.010 g1 (0.13)

–0.002 (0.36)

0.489 (6.87)

0.734 (90.71)

0.463 0.074

1.96

SUR

1971–2008 0.048 g2 (1.33)

–0.088 (2.39)

0.489 (6.87)

0.734 (90.71)

0.651 0.035

1.71

SUR

1971–2008 0.0054 g3 (1.10)

–0.255 (5.05)

0.489 (6.87)

0.734 (90.71)

0.707 0.047

1.70

SUR

1971–2008 0.070 g4 (2.30)

–0.080 (2.59)

0.489 (6.87)

0.734 (90.71)

0.719 0.029

1.45

SUR

1971–2008 0.029 g5 (0.80)

–0.186 (5.02)

0.489 (6.87)

0.734 (90.71)

0.647 0.035

1.72

SUR

1971–2008 0.024 g6 (0.84)

–0.080 (2.67)

0.489 (6.87)

0.734 (90.71)

0.736 0.028

1.29

SUR

rl-rs

GAP indicates that the dependent variable was the output gap while g denotes that it was the rate of output growth. The last six equations represent sectoral output growth. g1 denotes agriculture and fishery, g2 manufacturing, g3 construction, g4 trade, g5 financial and real estate services and g6 production of public services. The standard error of estimate (SEE) has been multiplied by 100 for the equations using aggregate economy data. The sectoral equations have been estimated using the restriction that the coefficients for lagged output growth and world output growth are equal. re is the real exchange rate, rs the real short rate of interest and rl the real long rate.

The interesting result from the point of view of the previous discussion is that λ for the whole period or recent years has risen noticeably to values close to the external trade share.

3.2

The advent of the euro area

In general, the equation performs better for the most recent (post1998) sample than for the whole sample; at least if we focus on the role





data

Estimation of an extended IS curve lag

world

rr

re

hp

sp

R2/SEE

DW/J

1971–08, ∆y

0.679 0.374 (27.54) (11.38)

–0.006 (0.55)

0.018 (4.08)

0.685 0.138

1.99

OLS

1987–08, ∆y

0.678 (22.51)

0.381 (8.20)

–0.038 (3.20)

0.030 (5.35)

0.739 0.114

2.19

OLS

1987–08, ∆y

0.711 0.409 (39.08) (13.51)

–0.028 (3.29)

0.025 (6.80)

0.737 0.113

2.25

GLS

1979–08, ∇y

0.669 0.416 (23.86) (10.56)

–0.003 (0.35)

0.001 0.024 0.001 (0.37) (7.85) (0.67)

0.715 0.068

2.23

OLS

1979–08, ∇y

0.243 (9.56)

0.747 (6.91)

–0.047 (1.65)

0.036 0.047 –0.003 (1.90) (9.63) (4.85)

.. 0.0076 7.31

GMM

1979–08, ∆y

0.614 (25.06)

0.324 (7.77)

–0.007 (0.60)

0.032 0.044 0.007 (6.73) (8.87) (4.75)

0.798 2.14 0.0097

OLS

1979–08, ∆y

0.410 (16.31)

0.185 (0.88)

–0.040 (3.92)

0.149 0.077 –0.008 (1.49) (7.42) (0.99)

.. 0.0124 8.93

GMM

1979–08, ∆y

0.565 (13.61)

0.231 (3.57)

–0.008 (0.44)

0.030 0.056 0.010 (4.68) (7.77) (3.80)

0.758 1.77 0.0101

OLS gap>0

1979–08, ∆y

0.605 (18.09)

0.412 (7.47)

–0.005 (0.33)

0.041 0.036 0.007 (5.56) (4.74) (3.94)

0.828 1.68 0.0088

OLS gap0

1979–08, ∆y

0.643 (17.89)

0.306 (4.52)

–0.001 (0.04)

0.045 0.072 0.009 (5.42) (4.40) (4.13)

0.804 1.85 0.0092

OLS hp

–16

–40 1980

1985

1990

1995

2000

2005

Note that the data period has been extended slightly to include more recent observations.

If, however, we difference the model to enable us to use the ArellanoBond GMM panel estimator, the results become a little less satisfactory (see the last column in Table 3.5). Both the interest rate and stock price terms become insignificant. It is not allowing for the simultaneous relationships through GMM which creates the problem. Indeed the GMM results are more plausible than their least squares counterparts. Our estimation period has been chosen by the maximum length of the data series available, rather than by any clear choice based on the existence of a single regime. Extending the model back to 1970 (while omitting the asset price terms) gives some problems with the exchange rate effect (Table 3.6, column 2), as does omitting the fixed effects (columns 3 and 4). Restricting the sample just to the euro area period (column 6) suggests that the interest rate has become less important. This is usual for a very credible regime (Blinder and Solow, 1973). With inflation rates approximately on target throughout the estimation period it is not really surprising if inflation has been relatively unimportant. Similarly it is not surprising to see that the stock price effect looks weak, since there was a substantial fall and recovery in most stock markets in that period, without any substantial effect on



Figure 3.7

60 Asymmetry and Aggregation in the EU Basic IS curve specification with different lags 1

2

3

0.871 (19.92)

0.820 (17.77)

0.835 (17.83)

re

0.023 (8.07)

0.027 (8.90)

rr

–0.055 (2.91)

hp sp

∆yw

5

6

7

0.320 (7.01)

0.309 (8.35)

0.756 (9.57)

0.418 (3.04)

0.026 (8.69)

0.010 (4.85)

0.006 (3.69)

0.021 (2.96)

0.016 (3.32)

–0.074 (4.30)

–0.055 (3.37)

–0.032 (2.69)

–0.021 (2.13)

–0.028 (1.00)

–0.005 (0.09)

0.100 (12.97)

0.094 (12.67)

0.096 (12.75)

0.035 (6.13)

0.023 (5.31)

0.081 (5.54)

0.068 (3.39)

0.006 (3.02)

0.009 (4.53)

0.008 (4.96)

0.008 (5.41)

0.007 (6.24)

–0.002 (0.62)

–0.008 (0.85)

0.630 0.679 (21.43) (33.72)

–0.248 (5.43)

0.360 (7.21)

y–1 R2 SEE DW Estimator Panel Lags

0.623 0.0138 0.638 LS CFE 0,0

0.632 0.0136 0.644 LS CFE 2,4

0.629 0.0137 0.643 LS CFE 2,2

4

0.802 0.0100 2.127 LS CFE 2,2

0.800 0.0099 2.223 GLS CFE 2,2

0.191 0.1039 2.095 LS Dif 2,2

.. 0.0125 .. GMM Dif 2,2

The dependent variable is the growth rate of GDP, denoted by ∆y. Number of observations is 1037 (with first differences, the number is 1022). Numbers in parentheses are corrected t-ratios. Lags denote the fixed lags of re and rr, respectively. CFE denotes the inclusion of fixed effects, Dif indicates that the data are differenced, LS denotes ordinary least squares and GLS, generalised least squares, while GMM denotes Generalised Method of Moments (Arellano-Bond) estimator. Then the J-statistic has the value of 9.28 that is far from significant with the instrument rank of 15. If one tests the presence of fixed effects one can typically reject the hypothesis that these effects are identically equal to zero. Thus, e.g. in the case of equation (4) above the value of the F-test statistic is 7.80 which is significant at all conventional levels.

output.13 In part this reflects the offsetting monetary policy. However, to some extent this can be circumvented by including policy in the model as we go on to do and in part the endogeneity will be accounted for in the GMM estimates. A glance at Figure 3.8, suggests that the results obtained from using the output gap instead of output growth will be fairly similar as the two series 13

Shortening the estimation period to just eight years so that we incorporate only one business cycle is likely to lead to data specific problems. Even with the 28 years for our main estimation the period is somewhat shorter than might be ideal for purely statistical purposes but extending the data period also increases the chance of encompassing a regime change.



Table 3.5

Aggregate Supply and Demand in an Open Economy 61 Comparison of different IS curve specifications 1

2

3

4

5

6

∆y

0.397 (8.93)

0.367 (10.48)

0.224 (7.63)

0.217 (8.12)

0.794 (9.29)

0.600 (6.33)

re

0.009 (4.00)

–0.001 (0.47)

0.001 (0.31)

0.001 (0.27)

0.021 (3.37)

0.017 (3.45)

rr

–0.059 (4.89)

–0.016 (1.13)

–0.023 (2.00)

–0.021 (1.93)

–0.043 (1.70)

–0.041 (1.38)

hp

0.028 (5.10)

0.018 (3.74)

0.060 (3.37)

0.044 (3.70)

sp

0.008 (5.18)

0.007 (4.36)

–0.002 (0.67)

0.004 (1.00)

0.759 (28.03)

0.760 (34.82)

–0.085 (1.83)

0.447 (7.34)

∆y–1

0.683 (24.05)

R2 SEE DW Estimator Panel Lags N

0.787 0.0104 2.107 LS CFE 2,2 1037

0.692 (20.36) 0.695 0.0139 1.927 LS CFE 2,2 1682

0.781 0.0105 2.219 LS None 2,2 1037

0.549 0.0105 .. LAD None 2,2 1037

0.096 0.0106 .. LAD None, dif 2,2 1037

0.791 0.0089 2.063 LS CFE 2,2 449

Variables and other labels defined as in Table 3.5. The dependent variable is ∆y. LAD denotes the least absolute deviations estimator. None denotes that no fixed or random effects are included, dif that the data (all variables) are differenced. If house and stock prices are not included, the sample size would increase considerably (i.e. from 1037 to 1682). Equation 6 is estimated from the sample of the EMU period 1999Q1–2006Q4.

have been moving quite closely together. However, this is not quite the case (Table 3.7). The stock market coefficient has a tendency to show a perverse sign and is significantly so at the 5 per cent level in the last two columns. The results are conventional if we take just the period of the euro area’s existence (columns 4 and 5). Nevertheless, whichever specifications we look at it is very difficult to suggest that housing prices are not clearly related to the growth rate and the run of results suggests that stock prices also are likely to have an effect, albeit clearly weaker. 3.3.2

The effect of asymmetry

Thus far all our results consider a symmetric approach, assuming that it does not matter whether the economy is in the expansionary or contractionary phases of the growth cycle. Both economic theory stretching back to Keynes (1936) and beyond and previous empirical results



Table 3.6

62 Asymmetry and Aggregation in the EU Figure 3.8

Median values of output growth and output gap

8

6

4

2

0

–4 1970

1975

1980

1985 output gap

1990

1995

2000

2005

output growth

(Mayes and Virén, 2000b) suggest that such symmetry is unlikely and we find the same to be true here. The economic cycle itself is asymmetric with recessions tending to be sharper, shorter and shallower than expansions, at least in recent years for most European countries in our sample14 if the Finnish crisis of the 1990s is excluded.15 On the whole the asymmetry in the cycle is attributed, not so much to asymmetry in the shocks which assail economies, although this is the case if wars are included, but to asymmetries in behaviour. Although negative shocks tend to be transitory and positive shocks permanent (Nadal De Simone and Clarke, 2007). Many sources have been identified, in labour markets,

14 Verbrugge (1998) provides a helpful exposition of the nature of asymmetry in the main macroeconomic variables in 22 countries, including most of those in our sample. 15 The crises in the other Nordic countries round the same period, although traumatic, did not involve major falls in GDP. Finland’s recession was however deeper than in 1929.



–2

Aggregate Supply and Demand in an Open Economy 63 Estimation of the IS curve with the output gap variable 1

2

3

0.457 (10.66)

447 (10.31)

0.466 (11.12)

re

0.003 (2.18)

0.002 (1.33)

rr

–0.010 (1.14)

–0.004 (0.41)

hp

0.023 (5.97)

sp

–0.001 (0.23)

0.002

0.003

0.641 (19.59)

0.601 (17.96)

0.663 (22.20)

∇yw

∇y–1 R2 SEE DW Estimator Panel Lags N

0.716 0.0070 2.199 LS CFE 2,2 1037

0.587 0.0103 1.929 LS CFE 2,2 1682

6

7

0.683 0.513 (8.13) (10.34)

0.797 (9.67)

0.699 (5.06)

0.004 (2.43)

0.022 (5.37)

0.014 (5.83)

0.007 (1.49)

0.015 (2.90)

–0.030 (3.39)

–0.010 (4.03)

–0.034 (2.89)

–0.052 (2.58)

–0.080 (1.18)

0.022 (2.97)

0.018 (3.94)

0.038 (4.09)

0.037 (2.07)

–0.005 (0.78)

–0.002 (1.77)

(2.31)

(2.01)

0.263 0.495 (4.04) (13.64)

–0.250 (4.84)

0.138 (2.44)

0.704 0.0071 2.163 LS CFE 2,2 1037

4

0.650 0.0058 1.923 LS CFE 2,2 449

5

0.618 0.0055 1.916 GLS CFE 2,2 449

0.167 .. 0.0074 0.0080 2.091 .. LS GMM Dif Dif 2,2 2,2 1022 1022

Variables and other labels as defined in Table 3.5. The dependent variable is the output gap, denoted by ∇y. Equations in the two last columns (4–5) are estimated from the sample of the EMU period 1999Q1–2006Q4. The value of the J-statistic is 10.74 which is not significant with the instrument rank of 15.

in productivity (Artis et al., 1999), in exit and entry (Chetty and Heckman, 1986; Baldwin and Krugman, 1989). The asymmetries in real behaviour and in inflation, while closely related, are different (Dupasquier and Ricketts, 1998, explore this for Canada, for example). Both fiscal policy and monetary policy have asymmetric elements to them (Mayes and Virén, 2004, 2005). Given this rich background, there are several ways in which we could introduce asymmetry. Their appropriateness depends on the specification of the model and the extent of the data we have to hand. One approach is simply to follow the framework of Sims and Zha (2006) and assume that there is a regime switch that corresponds to the up and down phases of the cycle. This would imply that we simply estimate two different models depending upon the phase. These could perhaps explain the phenomenon that Keynes noted that recessions tended to be shorter and sharper than expansions. A second possibility is to assume that there is more than one



Table 3.7

equilibrium, as in Sargent (2001) for example; where in one case the economy is dominated by optimistic expectations and in the other by pessimistic expectations – shocks driving them from one to the other. There is some attraction in this approach in the context of asset prices. One way of explaining the bull and bear phases of the stock market would be to use expectations in this manner. As forward-looking prices they will be heavily affected by changes in expectations. A further possibility would be to consider the difference in constraints that appear in the up and down phases by using a form of Friedman’s (1968, 1993) plucking model, applied in Nadal De Simone and Clarke (2007) and Kim and Nelson (1999) for example. Here the assumption is that there is some maximal rate of growth determined by capacity and underlying technologies but that shocks drive the economy below that attainable level hence there is different behaviour when the economy is recovering from a shock from when it is running close to capacity. The model therefore finds that negative shocks tend to be temporary whereas positive shocks are more likely to be permanent, both driving the economy upwards and leading to clearly different behavioural responses. Housing (property) cycles might fit quite neatly into this framework as there are strong capacity constraints limiting the rate of expansion, with considerable lags involved. Moreover, given the interaction with financial markets, the up and down phases are characterised by rather different behaviour. When the market starts to go down people are inhibited from selling as otherwise they might realise collateral prices that are relatively low compared to the loans used to purchase. Indeed in some cases equity can become negative. This generates a complex interaction between prices and quantities. From the point of view of economic growth it is new construction that matters (in net terms at any rate) whereas prices reflect both the existing stock and new construction and are heavily dominated by the former. All these various models explain why we should expect different behaviour over the cycle and between them suggest two general ways in which we might represent them. The first is simply to suggest that the coefficients are different in the two phases. The second is to assume that there is a single equilibrium but that adjustment to it varies according to the phase of the cycle. Thus, for example, the reaction to a downward shock may be more rapid than to a positive shock which leads to an extended period above the longer-term equilibrium; see Enders and Siklos (2001) for example. We have explored this in Huang et al. (2001) in the case of monetary policy. Moreover, the switch between regimes may be a smooth transition with coefficients changing gradually over a number of periods, rather than an immediate switch from one to the other.




This gives us a considerable problem in choosing the best representation as the adjustment in behaviour will be spread across a number of equations in the model. Since we are limiting our main focus to the IS curve and the behaviour of monetary policy, we have opted for a straight forward approach, which is a version of the first group described above, namely to assume that the coefficients in the model are different in the two phases. To do this we introduce asymmetry through a threshold model (Tong, 1983; Teräsvirta and Granger, 1993) as discussed in Chapter 2. This means that we allow the variables of interest: the real exchange rate, real interest rate, house prices and stock prices to have different values if the economy is contracting from when it is expanding (Table 3.8). It is immediately apparent that all the variables have clearly different effects in expansions compared to contractions, with the exception of stock prices.16 The nature of the effect is interesting as all variables except foreign growth have a greater impact in an upturn than in a downturn. One possible way of thinking about this is to suggest that in expansions there will always be an element of capacity constraints that do not apply in a downturn. Thus there is some restraint in the way in which the economy can respond to a change in foreign demand. Interest rates and the exchange rate could be expected to have the same characteristics in some sort of real equivalent of the Phillips curve, where policy becomes less effective when the economy is relatively slack. Clearly we can produce arguments for other forms of asymmetry. For example, that producers will struggle to retain markets even if they are making short-run losses, because it will be much more expensive to try to enter a market having exited, as many contacts will be suspicious about the continuity of future supply. In Table 3.9 we consider a different form the asymmetry might take. In Table 3.8 we defined the cycle in terms of the growth of GDP. We can also consider it in terms of the direction of change of asset prices. This gives a much more direct representation of the change in expectations. We look in particular at the role of the real interest rate as representing the main monetary policy variable. If house or stock prices

16

The coefficients are jointly different in the two phases as indicated by a Wald test. In addition to the switching regression threshold model we have examined the results using a Smooth Transition Regression (STR) model where a logistic function is used to transform the transition variable. See Teräsvirta and Granger (1993) for details. Because the results with STR model were almost identical with the switching regression threshold models we do not report them here.




66 Asymmetry and Aggregation in the EU Asymmetry in the IS curve 1

2

3

4

∆yW

0.366 (6.30)

0.260 (3.47)

0.365 (6.94)

0.280 (5.11)

re

0.013 (4.35)

0.038 (1.80)

0.010 (4.41)

0.016 (0.55)

rr

–0.030 (1.86)

–0.060 (2.93)

–0.029 (1.98)

–0.042 (2.75)

hp

0.018 (2.36)

0.051 (5.86)

0.011 (1.88)

0.032 (5.22)

sp

0.010 (5.19)

0.009 (3.24)

0.010 (6.02)

0.007 (3.52)

0.636 (17.80)

0.575 (10.08)

0.659 (25.81)

0.615 (16.18)

∆y–1 R2 SEE DW Estimator Panel Lags sample N

0.830 0.0090 1.611 LS CFE 2,2 gap≤0 562

0.762 0.0106 1.973 LS CFE 2,4 gap>0 475

0.830 0.0089 1.621 GLS CFE 2,2 gap≤0 562

0.757 0.0104 1.798 GLS CFE 2,4 gap>0 475

The dependent variable is output growth. Notation is the same as in Table 3.5. Using the Chow test; parameter equality can be rejected (Thus, in the case of equations 1 and 2, F(21,106) = 3.31)).

are falling the real interest rate has a much more limited effect on output than when they are rising. This may help explain why in Chapter 8 on monetary policy, we find that interest rates change more vigorously in the down phase of the cycle. However, the coefficients are not well determined in the case of rising prices. Since we are looking here at European monetary policy this has nothing to do with any ‘Greenspan effect’. There has not been any suggestion that European countries have responded to house and stock market prices in the same explicit manner as has been developed in the US. What we see here, however, is a justification in Europe for just such an asymmetric policy response to asset price movements. Of course we have to take both the asymmetry in the asset price movements themselves as well as in interest rates to judge the policy



Table 3.8


Dep var → ∆yw

The effect of house and stock prices: An update ∆y

∆y

∆y

0.281 (6.80)

0.318 (8.02)

0.289 (7.65)

gap

gap

gap

0.417 (10.38)

0.423 (10.70)

0.342 (10.76)

0.615 (2.97)

0.078 (0.61)

0.213 (1.66)

0.145 (1.08)

∇yw re/100

0.888 (4.13)

0.668 (3.35)

rr/100

–0.027 (2.99)

–0.037 (3.13)

–0.011 (1.29)

–0.012 (1.53)

hp

0.036 (7.49)

0.021 (4.38)

0.023 (7.14)

0.013 (3.28)

rr|hp0 rr

3

–0.022 (0.73)

0.270 (7.91) 0.263 (7.75) 0.329 (9.18) –0.030 (1.99)

rr|x>0 0.015 (2.40) sp 0.004 (2.22) ∆cq–1 0.604 (19.05) R2 0.787 SEE 0.0095 DW 1.953 Estimator LS Panel CFE Period 79–07 x .. N 843

0.023 (1.87) 0.001 (0.45) 0.603 (11.60) 0.793 0.0088 1.989 LS CFE 99–07 .. 450

0.015 (2.34) 0.002 (1.61) 0.598 (18.87) 0.791 0.0094 1.980 LS CFE 79–07 gap 843

6 0.270 (8.04)

7 0.264 (7.82)

0.309 (7.45) 0.250 (6.93) –0.026 (1.63)

rr|x≤0

hp

5

0.021 (2.65) 0.004 (2.23) 0.600 (18.76) 0.789 0.0095 1.954 LS CFE 79–07 hp 843

–0.034 (1.46) –0.028 (1.70) 0.014 (1.97) 0.004 (2.22) 0.604 (19.02) 0.788 0.0095 1.952 LS CFE 79–07 hp 843

–0.042 –0.058 (2.19) (3.01) –0.015 –0.003 (0.70) (0.20) 0.011 0.014 (1.51) (2.19) 0.003 0.001 (2.24) (0.69) 0.604 0.610 (19.00) (19.25) 0.788 0.790 0.0094 0.0094 1.951 1.969 LS LS CFE CFE 79–07 79–07 hp 0. With the unemployment variable, the coefficient turns out to be almost zero when unemployment is large. The rate of inflation is computed using either the Consumer Price Index or the GDP deflator. The sample period is 1983–2007.

–0.545 (13.35)

–0.107 (1.76)

CPI, SD

0.718 (3.94)

CPI, SD

–0.088 (0.81)

CPI, SD

u–u*

–0.550 (6.12)

0.363 (5.91)

CPI, range

y|y>0

CPI, SD

0.356 (5.72)

Output y

Backward-looking Phillips curve with regional data

CPI, SD

Data

Table 5.2

122

Regional and Sectoral Concerns 123 Estimates using unemployment rather than the output gap (1) ∆p

e

(2)

0.655 0.649 (12.42) (10.17)

(3)

(4)

0.513 (12.72)

0.488 (11.77)

(5)

(6) 0.522 (13.76)

∆p–1

0.254 (5.72)

0.214 (3.92)

0.191 (5.26)

0.143 (3.66)

0.567 (23.89)

0.187 (5.31)

∆m

0.058 (6.56)

0.056 (5.43)

0.063 (10.14)

0.068 (9.25)

0.085 (13.19)

0.065 (10.54)

–0.053 (3.36)

–0.003 (0.23)

–256 (10.06)

–248 (12.84)

–290 (11.82)

–306/–0.260 (11.66/12.00)

U Umax-Umin

0.068 (4.81)

Usd

0.147 (6.47) 0.103 (2.32)

t

0.154 (6.86)

0.130 (5.91)

0.192 (2.93)

–0.016 (1.82)

–0.001 (0.45)

–0.112 (10.06)

–0.108 (9.05)

–0.112 (9.25)

–0.110 (10.57)

R2

0.868

0.866

0.914

0.918

0.885

0.918

SEE

0.963

1.073

0.816

0.878

0.938

0.797

DW

1.526

1.289

1.800

1.590

1.928

1.822

Dummies

No

No

yes

yes

Yes

Yes

Obs

153

143

153

143

153

153

All estimates are SUR estimates. ∆ p e denotes expected inflation (OECD forecasts), m import prices, ∆p is inflation in consumption prices, U the aggregate unemployment rate, Umax-Umin the range of regional unemployment rates, Usd the corresponding standard deviation and t time trend. Column (6) is estimated using a threshold model specification and allowing the coefficient of the unemployment rate to vary depending on whether the rate is below (first coefficient) or above the 10.8 per cent (second coefficient) threshold. The hypothesis that the coefficients are equal can be rejected with marginal probability of 0.0013 per cent using the F test.

gaps for each of the four industries separately,7 for all four sectors the impact on inflation is higher when there is a positive output gap (see the last four rows of Table 5.4). In each case the positive segment coefficient is clearly significantly different from zero. In the case of agriculture and construction the impact is relatively limited. The negative segment coefficients are close to zero and poorly determined, with the exception of services where there is a moderate affect. The sectoral distribution of any excess supply thus has an effect on the overall

7

Inflation also relates to the sectoral prices.



Table 5.3


Output dispersion

Estimates of a nonlinear Phillips curve with output dispersion

pm

p–1

∆tax

Dispersion

y

y0

∆p

SD/ range

SEAS D3

R2/ SEE

DW

1 SD

–0.061 (1.84)

–0.505 (12.64)

0.063 (12.64)

0.002 (2.78)

0.256 0.0050

2.29

2 range

–0.063 (1.86)

–0.495 (12.42)

0.017 (6.59)

0.002 (2.82)

0.258 0.0050

2.31

3 SD

–0.201 (2.58)

–0.525 (12.73)

0.045 (4.33)

0.002 (2.81)

0.269 0.0049

2.27

0.285 (2.47)

Estimating equation ∆2pt+1 = a1*(ut–ut*) + a2pt + a3SDt + a4∑SEASit.

where p = log(CPI), ∆12p = annual inflation, and ∆2p second difference of p. SD = standard deviation of regional unemployment rates, RANGE is the range of Finnish regional unemployment rates GMM estimates for New-Keynesian hybrid Phillips curve. What is striking about these results is not just the importance of the dispersion of unemployment but that the influence of unemployment varies so clearly across the threshold. In each case the unemployment



(5.6)

Regional and Sectoral Concerns 133


term is correctly signed and the dispersion term contributes to a closer explanation. The evidence for the importance of dispersion seems overwhelming and hence the treatment of Phillips curves without regard either to the dispersion of unemployment or to the dispersion in sectoral performance are likely to be misleading for decisions on aggregate policy. Clearly one way to deal with this is to consider the problem at a more disaggregate level and then aggregate taking into account the differences among the nonlinear estimating equations. However, simply by looking at measures of dispersion it is possible to perceive a more nuanced picture. Increases in dispersion in both unemployment and sectoral dimensions increase inflationary pressure. Insofar as there is going to be increasing integration in the EU then these pressure from dispersion will become progressively less important. But if there is going to be further substantial structural change this may offset at least part of the lessening impact through convergence.


6

After the Phillips curve, the Okun curve – the relationship between output and unemployment – is probably the most obviously nonlinear. In the growth phase of the economic cycle unemployment tends to fall steadily until the point when labour shortages are reached. In the downturn, even if it only represents slow growth, unemployment tends to rise disproportionately but with a lag. This inter-relationship, which is reflected in fluctuations in labour (and total factor) productivity, lies at the heart of the real business cycle literature and it has proved difficult to provide a totally convincing explanation that covers the whole cycle, the shocks that are experienced and the leads and lags that affect behaviour. Hiring and firing have costs. Hence firms try to take a longer-term view of their labour needs. In periods of rapid expansion they prefer to get the workforce to work longer hours and seek means of increasing productivity, albeit temporarily, if they think that the growth may be exceptional. Similarly in a downturn, if the pause is expected to be relatively short-lived, they will attempt to hold onto their labour force and reduce costs by cutting back on hours worked and limiting pay increases. One of the characteristics of the present recession in Europe, unlike the United States, is that unemployment has risen by less than what might otherwise have been expected from previous, milder fluctuations. In part, this is probably due to the concentration of the problems in the financial sector but it no doubt also represents a general feeling that demand will bounce back, particularly in the investment goods sectors, as soon as the banking problems are sorted out. Clearly the problems vary by individual country, depending not only on the structure of their industry but also on the size of their banking problems and whether they had been subject to a housing boom in the period leading up to the crisis. In any event the 134



Output, Unemployment and the Labour Market: The Okun Curve

crisis is not over yet and there are fears that there will be a double dip recession as the emergency measures both to save the banking system and to bolster demand temporarily are unwound. This has been exacerbated by the realisation that some projected debt levels, particularly in Europe, are unsustainable and hence need to be addressed sooner rather than later. Such a second contraction would be much more likely to result in more substantial falls in employment as optimism declines and the ability to bridge the temporary slowdown falls. Such speculations lie beyond the scope of this chapter as our data are predominantly for the 30-year period before the crisis, during which fluctuations in most western European countries were more limited and characterised more by oil or other external shocks. There is one exception, the Nordic countries, which experienced a previous financial crisis at the beginning of the 1990s. However, this is a single episode, concentrated in just three countries in our data set, and, hence, is unlikely to be sufficient to characterise the estimates for the data as a whole. In the rest of this chapter, we therefore begin by setting out the characteristics of the Okun curve and how we treat it, before going on to look at the behaviour of Okun curves both for our panel of countries and for them individually. We conclude with a wider consideration of the problems that the asymmetries in behaviour that our analysis reveals pose for economic policy, particularly in the light of the lack of success of the Lisbon Agenda to increase the rate of growth and the new problems that the crisis has added to those of ageing and competition from China inter alia.

6.1

The specification and estimation of Okun curves

The Okun curve has been the subject to several recent studies (Attfield and Silverstone, 1998; Harris and Silverstone, 1999a, b; Kaufman, 1988; Moosa, 1997, Palley, 1993, Prachowny, 1993 and Weber, 1995, for example) so we have plenty of experience to draw upon. There is clear experience of asymmetry. For example, Harris and Silverstone (1999b) find asymmetry of some form for Australia, Japan, New Zealand, the UK, US and West Germany over the period 1978 to 1999.1 However, the finding is not universal and they cannot reject the null hypothesis of symmetry for Canada over the same period. Perhaps more interesting is

1

Silvapulle et al. (2004) and Cuaresma (2003) also find asymmetry for the US over the period since the war.



Output, Unemployment and the Labour Market: The Okun Curve 135


the much more detailed treatment of the relationship by Haltiwanger and Schuh (1999), who introduce sector specific factors to help explain the lack of symmetry. We deal with the aggregated relationship first. The Okun curve (Okun, 1962) is normally expressed as the relationship between the change in unemployment and the percentage change in real output in the economy (6.1)

However, it is also argued (see Prachowny (1993) for example) that some scaling of the labour variable is required so in our formulation we have also included population of working age, POP.2 The curve may therefore offer some additional insight into the nonlinear operation of the labour market to augment the Phillips curve results of the previous section. Both employment and unemployment appear to respond in an asymmetric manner to demand shocks. We focus on the Harris and Silverstone approach, as rather than estimating a curve or piece-wise linear function they build the asymmetry into the error correction mechanism, assuming that there are different correction paths depending upon whether real output is above or below its trend value. In effect, therefore this gives us three different ways of handling the asymmetry. The first, following Kim and Nelson (1999) is to assume that although the function itself is linear, we should treat potential output more in the form of a frontier, very much along the lines of frontier production functions (Aigner et al., 1977; Mayes et al., 1994; Mayes, 1996). This provides a direct extension to Prachowny’s (1993) production function basis for the Okun curve. Here the errors in the relationship can be decomposed into a symmetric term e and a nonsymmetric term v, which permits a longer tail of values when the economy is operating inside the frontier. Thus in the case of (6.1) the error term u in the estimated relationship ∆U = c0 + c1∆Y/Y + u

(6.2)

would be composed u = v + e, with e ~ N (0, σ2e) and v ~ M (µ,σ2v) where M is a nonsymmetric distribution.3

2

Prachowny (1993) employs a rather more elaborate transformation, incorporating the Okun curve into a production function and considering linear discrepancies of the factors of production from their equilibrium values as well. 3 In line with the early frontier production function Kim and Nelson (1999) assume that M is half Normal, Mayes et al. (1994) also consider the more general case of a truncated normal.



∆U = c0 + c1∆Y/Y

Output, Unemployment and the Labour Market: The Okun Curve 137

The second approach, used by Harris and Silverstone (1999a, b), is to estimate the cointegrating relationship in (6.2) and assume that the error correction mechanism ε ε = U – cˆ0 – cˆ1 y

(6.3)

where ^ denotes an estimate, can be divided into ε where ε < 0 and ε+ ≥ 0. The coefficients in the equations explaining ε– and ε+ are then not constrained to be equal in the adjustment process. Our approach and that of Laxton et al. (1999) and Pyyhtiä (1999) is to treat the relationship itself as being nonlinear and hence we use the more conventional threshold model in terms of output growth –

(6.4)

In (6.4) y is the growth rate in GDP,4 pop the population of working age and ε the error correction term defined in (6.3) (lagged one period). t is a threshold value for the asymmetry. There are a number of routes to determining this threshold. One would be simply to use a simple form of output gap, although since our data are annual this would entail a fairly trivial definition of potential output, such as the mean rate of growth over the sample period. We show the effects of setting t equal zero so that we distinguish actual recessions from other behaviour and determining the maximum likelihood value for t. In the second case the outcomes tend to be near the mean. Each of the three approaches gives a somewhat different flavour to the problem but it is possible to take the study of asymmetry further as demonstrated by Holmes and Siverstone (2005), who suggest that the asymmetry has two facets. First of all, the response depends upon whether output is rising or falling but, secondly, it also depends upon whether the economy is above or below trend. This can be considered in terms of output or unemployment. It is really only with the United States that there is enough data available to estimate this more complex relationship, as over 40 years of data and five cycles are available for study in the period since the early 1960s (not counting the present downturn). What Holmes and Silverstone find is that unemployment is more sensitive to changes in output in periods when activity is below trend than when it is above. Taken simply this might imply that when the

4 We also use the output gap as the measure of output in some regressions as a test of the robustness of output growth.



∆U = c0 + c1y+ + c2y– + c3 ∆pop + c4ε–1 + ut

labour market is under pressure output growth has to be largely driven by productivity if it is to succeed. Correspondingly if there is a downturn in activity firms will tend to hoard labour, at least initially, because of the difficulty of recruiting again in such an environment. Taken together therefore this gives four phases over the course of the cycle. As activity begins to fall away from the peak, unemployment will increase and at faster rate than it fell per unit of output in the period running up to the peak. Once output falls below trend the rise in unemployment for each given fall in output will increase. Once the bottom of the cycle has passed and output begins to pick up then unemployment will begin to fall but again by less than it rose in the latter stages of the downturn per unit of output change. Once output rises enough that it moves above trend levels, unemployment will fall more slowly for each increase in output until eventually the economy peaks and the process is repeated. Holmes and Silverstone (2005) suggest that this double asymmetry may explain the jobless growth of the recovery after 2001 as the US economy was rapidly back over trend levels of output. Koenders and Rogerson (2005) offer a somewhat different explanation based on the length of the previous period of expansion – the longer the period of expansion then the smaller the employment growth thereafter. However, the Holmes and Silverstone explanation sounds the more plausible, as it relates to the state of the labour market. Such findings would also help explain why, in the EU, unemployment has not risen as steeply as one might have expected in the present recession. It is however more difficult to explain the US experience where unemployment has risen sharply. Perhaps a more complex view of the dynamics is required which takes expectations into account as well.

6.2

Estimates of the Okun curve

Our principal results are shown in Table 6.1. As expected there is a clear relationship between output, population and the level of unemployment across our sample of countries. There is also a trend in the data. As expected, increases in output decrease unemployment while increases in population increase it. The same applies if we look just at the changes in the relationship as in the first seven rows in the table. This seems largely invariant to estimation method or to the choice of time period. The relationship is clearly asymmetric with unemployment responding noticeably less to changes in output above the threshold than it does below, whether we chose to distinguish between positive and negative





0.156 (2.62) 0.334 (5.75) –0.424 (1.62) 0.258 (5.51) 0.302 (4.98) 0.303 (4.41) 0.271 (2.42) 0.260 (2.76) 0.314 (3.30)

1971–2008 1999–2008 1999–2008 1971–2008 1971–2008 1971–2008 1971–2008 1971–2008 1971–2008

–0.143 (6.64)

–0.182 (12.53)

–0.205 (12.53)

–0.158 (13.23)

Output

–0.128 (3.03)

–0.217 (4.51)

–0.238 (1.84)

–0.302 (6.21)

–0.171 (15.05)

–0.248 (6.71)

Output x 0 > β2. It is clear from Table 7.1 that the coefficient of β1 is positive except for a couple of cases and in all cases larger than the coefficient β2. β2 is negative except for Australia, France, Italy and Norway and in these cases the β2 coefficients are not significant. The estimation and test procedures made use of a GAUSS routine, available on Bruce Hansen’s homepage: http://www.ssc.wisc.edu/, which searches for the value that gives the smallest residual variance. According to the estimation results the threshold value of the public sector (output) size varies between 10 and 30 per cent. Koskela and Virén (2000) report that the threshold value is higher with the public sector share of total



The implications for using extra public spending to attempt to solve problems of unemployment thus face a prima facie difficulty. If this spending is not abnormally productive it will tend to result in a net loss to society, which may in itself tend to increase unemployment. Indeed the general wealth of the economy might be increased by a shift towards less public spending. It would depend on the nature of the redistributive process both in incomes and employment whether this diminished or increased unemployment, social exclusion or some other measure of social deprivation – a point not amenable to macroeconomic analysis. Using annual data for 22 OECD countries for the period 1960–96, it is possible to show (Table 7.1) first of all that a nonlinear model can represent the relationship between public sector output and overall output and secondly that over the relevant range the relationship is negative. The equation fitted is a simple application of the Granger and Teräsvirta (1993) threshold model (see Chapter 2). Denoting private sector output by D and public sector employment by Lg, we can divide the observations on the variables in (7.1)

162 Asymmetry and Aggregation in the EU Threshold model estimation results G/Y as the threshold variable ˆβ1

ˆβ2

SEE/DW

FHO

FHT

LM

Australia

0.365 (1.74)

0.049 (0.51)

0.025 (2.069)

18.9 (0.051)

9.7 (0.016)

2.14 (0.154)

Austria

0.580 (1.71)

–0.568 (1.69)

0.019 (1.759)

20.4 (0.046)

11.52 (0.003)

0.39 (0.538)

Belgium

0.690 (2.34)

–0.119 (0.48)

0.023 (2.159)

36.7 (0.000)

7.5 (0.119)

0.120 (0.283)

Canada

0.370 (1.57)

–0.751 (1.26)

0.027 (1.714)

4.9 (0.865)

4.3 (0.663)

2.86 (0.104)

Denmark

0.113 (0.80)

–0.700 (2.86)

0.024 (1.833)

19.2 (0.046)

7.2 (0.167)

1.63 (0.212)

Finland

0.458 (1.68)

–1.144 (2.25)

0.032 (1.648)

10.6 (0.308)

3.6 (0.876)

1.25 (0.274)

France

1.417 (3.23)

0.121 (0.25)

0.017 (1.961)

12.9 (0.270)

8.7 (0.028)

0.002 (0.966)

Germany

–0.063 (0.80)

–1.537 (3.64)

0.023 (1.767)

14.6 (0.138)

6.3 (0.283)

0.98 (0.331)

Greece

0.933 (1.98)

–0.354 (1.39)

0.031 (1.734)

26.6 (0.007)

11.6 (0.003)

0.16 (0.696)

Iceland

0.138 (0.61)

–1.021 (1.61)

0.040 (1.813)

5.25 (0.862)

3.8 (0.830)

0.95 (0.338)

Ireland

–0.109 (0.44)

–0.941 (1.89)

0.029 (1.947)

7.1 (0.697)

7.2 (0.177)

0.04 (0.845)

Italy

1.278 (3.28)

0.293 (0.99)

0.022 (1.785)

12.7 (0.221)

5.9 (0.415)

0.89 (0.354)

Japan

1.325 (2.16)

–0.880 (2.55)

0.024 (2.366)

24.3 (0.024)

6.8 (0.237)

2.74 (0.108)

Netherlands

0.156 (0.67)

–1.617 (4.37)

0.013 (1.868)

24.6 (0.040)

6.3 (0.210)

0.27 (0.605)

New Zealand

0.418 (1.06)

–0.697 (1.69)

0.037 (2.047)

15.8 (0.129)

6.1 (0.360)

0.04 (0.853)

Norway

0.448 (1.54)

0.159 (1.01)

0.019 (1.642)

7.6 (0.663)

7.3 (0.131)

5.05 (0.033)

Portugal

0.169 (1.37)

–0.153 (1.23)

0.032 (2.076)

7.7 (0.521)

3.5 (0.935)

0.28 (0.603)

Spain

0.186 (1.14)

–0.172 (1.57)

0.020 (2.272)

17.6 (0.096)

5.8 (0.359)

0.31 (0.584)

Sweden

0.330 (1.90)

–0.123 (0.88)

0.022 (1.673)

12.8 (0.222)

7.7 (0.117)

3.99 (0.055)

Country



Table 7.1

Asymmetry and the Role of the Public Sector 163 Table 7.1 Threshold model estimation results G/Y as the threshold variable – continued ˆβ1

ˆβ2

SEE/DW

FHO

FHT

LM

Switzerland

0.325 (1.06)

–0.904 (2.05)

0.022 (1.407)

15.2 (0.106)

5.4 (0.449)

7.51 (0.010)

UK

0.628 (1.74)

–0.131 (1.04)

0.024 (1.488)

7.9 (0.636)

4.6 (0.681)

12.41 (0.002)

USA

0.551 (1.62)

–0.008 (0.03)

0.024 (1.594)

8.8 (0.491)

3.9 (0.876)

10.72 (0.003)

Country

consumption and lower with the public sector share of total employment. With all threshold variables, the threshold values are very similar for all countries suggesting that there is some invariance across countries. The results are of course rather tentative owing to the very small sample size for each country. This can to some extent be eased by pooling the data and estimating the panel of 22 countries by seemingly unrelated regression estimation (SURE), as is shown in Table 7.2. In addition to linear and threshold models Koskela and Virén also estimated a multiplicative specification of the following form: ∆log Dt = α + β∆log Lg,t–1 + γ∆log Dt–1 + φHt • ∆Lg,t–1 + et

(7.3)

where H denotes the threshold variable (G/Y). According to this specification the public employment effect depends on the interaction term Ht • ∆Lgt–1 and thus on the size of the government sector. According to our hypothesis φ should be negative. Using this specification we can compute the critical (or, in a sense ‘threshold’) value of this variable at which public sector employment growth has zero effect on private sector output growth. The results with panel data conform with the results from individual country data. With a linear model there is no relationship between public sector employment and private sector output, while with the threshold model quite a clear relationship is obtained. There is a similar relationship using the multiplicative specification (7.3) in which the public sector



Numbers inside parentheses below the coefficient estimates are t-ratios. SEE is the standard error of estimate and DW the Durbin-Watson test statistic (which here suffers from the bias caused by lagged dependent variable). FHO denotes the LM (F) test for no threshold and FHT the corresponding test for threshold allowing for heteroscedastic errors. Numbers inside parentheses below the F statistics are bootstrap probability values. Finally, LM denotes a LM test for first-order autocorrelation of residuals (corresponding marginal significance levels are inside parentheses). When computing this LM test we have utilised Chan (1993), in which it is shown that the threshold parameter is superconsistent and can thus be treated as a known parameter.

164 Asymmetry and Aggregation in the EU Table 7.2

Estimation results with panel data β/β1

Linear G/Y Threshold Eq (7.3) with H = G/Y

–0.020 (0.12) 0.131 (2.64) 0.404 (5.55)

β2

γ

–0.058 (1.35)

0.294 (8.59) 0.325 (9.35) 0.281 (8.35)

φ

SEE/R2

ˆ H –

–2.460 (5.85)

0.028 0.171 0.027 0.185 0.028 0.186

0.157 0.164

Data sources for Tables 9 and 10: Y Gross Domestic Product at current or constant 1990 prices, OECD National Accounts, CD-ROM, OECD, Paris. G Public consumption or public sector (i.e. producers of government services) production, both at current or constant 1990 prices, OECD National Accounts, CD-ROM, OECD, Paris. Lg Public sector employment (thousands of persons). Employment in the Public Sector, OECD 1982, Paris; OECD National Accounts, CD-ROM, OECD, Paris; and some national sources. Data available from Virén upon request. Lp Private sector employment (thousands of persons). (Data source as Lg.)

employment effect depends on the size of the public sector. When the size of the public sector increases, the employment effect diminishes and, after some critical value, becomes negative. The implied critical values are, in fact, quite close to the average threshold values in the context of threshold model estimation. There is other evidence to support these findings. Karras (1996) has estimated the optimal government size by exploring the role of public services in the production process. As the theoretical framework he takes the analysis by Barro (1990), according to which government services are optimally provided when their marginal product equals unity (the so-called ‘Barro rule’). He finds for a data set of 118 countries over the period 1960 to 1985 that in some cases government services are over-provided, in some cases under-provided and in many cases optimally provided. The optimal government size in the Barro sense is 23 per cent (±2 per cent) for the average country, which number, however, masks important differences across regions. Interestingly, this number is not very far away from the value of the thresholds shown in Tables 7.1 and 7.2. Our analysis is less ambitious in the sense that we do not study the welfare issues. The key conclusion from this section



All estimates are SUR estimates with panel data consisting of 736 data points. All equations also include country intercepts, which are not reported. The threshold models (columns 2–4) are estimated using the average values of the threshold variable from the single country models. With the multiplicative model (the last three set of estimates) the threshold values are derived from the estimates of β and φ.

Asymmetry and the Role of the Public Sector 165

in the context of current European macroeconomic policy is that it supports the current inclination to try to cut taxation of labour as part of the Lisbon Strategy and to increase growth and employment.

Stabilisation and policy coordination

Our second concern in this chapter is with the scope for improved outcomes that might stem from fiscal policy coordination under EMU and under the SGP in particular. If the ability to coordinate is increased then this may help offset some of the disadvantages from the inability to run an independent macroeconomic policy. (It is of course always debatable the extent to which there was scope for independent action by the smaller countries in the previous regime, as in the main they had to follow the German lead because their economies were so integrated.) The EU does not attempt fiscal coordination in a strict sense of the word – there are no directives to the member states telling them how fiscal policy is to be set as part of some annual ‘plan’ – but there is what the European Commission (2002) describes as ‘weak coordination’ through the Broad Economic Policy Guidelines (BEPG). Second there is a set of rules on how budgetary balances may be set, laid out in the SGP (described by the European Commission (2002) as ‘strong coordination’). The formulation of the BEPG is a complex annual process, orchestrated by the Commission, aimed at trying to ensure that the macroeconomic policies of the member states contribute to the overall goal of sustainable noninflationary growth that achieves full employment. Much of what is involved relates to structural policies, wage developments and labour market reform – the Cardiff, Cologne and Luxembourg ‘processes’ – but also involves the application of the SGP. While the BEPG have no legal force and rely on peer pressure for their achievement, the SGP does have some coercive powers, although despite breaches no penalties have as yet been imposed. The SGP has two main sides to it. The first is to try to ensure that the member states all achieve a strong and sustainable budgetary position. This involves progress each year towards having a low debt ratio. While the end point has not been defined the process involves trying to remain in surplus or in balance through the course of the cycle. In order to qualify for monetary union the member states were supposed to have a debt ratio of less than 60 per cent of GDP or be making sustainable progress to achieving that. While that state was somewhat liberally interpreted in 1998 when the original membership of the monetary union was decided it has nevertheless remained at the heart of the Commission’s



7.2

longer-term predictions. The second side to the SGP is the Excessive Deficit Procedure (EDP), which is designed to prevent deficits in any particular year exceeding 3 per cent GDP except in cases of severe economic difficulty, as applied to several member states in the present crisis. Like the BEPG the EDP is essentially forward-looking. If a country looks, in the view of the Commission, that it is going to run an excess deficit, then it has to take steps to try to avoid it. If these steps are not taken and an excess deficit appears, ultimately the member state has to make a noninterest-bearing deposit from which the remaining states benefit. This can ultimately be converted into a fine if action is not taken in a period of two years. The detail, as set out in European Commission (2002) as amended, need not concern us here. In Chapter 9 we look at the impact that this asymmetric EDP has in policy. In this chapter our concern is with coordination. Coordination in the SGP framework is largely a matter of the appropriate design of the system (Virén, 2000b). It is not realistic to think of negotiated decisions that would lead to one country following an expansionary policy in order to help offset a deflationary shock to another. This does not of course involve fiscal federalism, as this is not part of the current EU arrangements except in rather indirect manner through the structural funds. This is an area where the EU differs clearly from other countries and federal arrangements. Others have found the substantial ‘automatic’ transfer of resources from the ‘gainers’ to the ‘losers’ when shocks hit, on a scale not contemplated by the EU, to be a necessary part of the attack on social exclusion. The absence of such mechanisms in the EU has been a persistent source of criticism (see, for example, Feldstein, 1997). Politically, it is pretty clear that a substantial system of inter-regional transfers similar to those that apply in the US, Germany, Canada or other mature fiscal federations is implausible for the foreseeable future. This in itself constrains what might be possible in social policy, because with a budget capped at just 1.27 per cent of GNP, the EU level cannot aspire to engage in the forms of equalisation and redistribution that the economic theories of fiscal federalism would prescribe (see, notably, Oates, 1999). Yet it should not be overlooked that within member states, these mechanisms are, typically already well developed: Southern England, for example, manifestly transfers resources to the ‘North’, while in Italy the geographical transfer is North to South; Germany transfers from West to East, Ireland from East to West. Again, coordination can help to maximise the impact of such mechanisms.





1. The cyclical behaviour of the economies and the nature of shocks must be similar. 2. Countries must have similar prerequisites for policy actions. 3. The tax and transfer systems and the budgetary process must be similar so as to provide reasonably similar automatic stabilisers. 4. Forecasts and the assessment of the current situations must be sufficiently accurate. 5. Effects of fiscal policy actions must be reasonably similar and predictable. 6. The effectiveness of coordinated policy actions must be much larger than uncoordinated actions. 7. Different countries must share the same policy view (in terms of the instruments and objectives of policy). 8. Policy commitments must be enforceable in different countries. These requirements are all straightforward in nature. If problems are uncorrelated then joint action is less likely to be valuable. If countries do not behave in a fairly similar manner then having relatively uniform prespecified responses is unlikely to constitute an optimal policy. If we do not know what the impact of policy is going to be on the economies then it is much more difficult to decide what to do. Perhaps

2 The normal definition of automatic stabilisation relates to the fact that the tax and benefit system, widely defined, is contra-cyclical in nature. As the economy slows, tax revenues slow more than proportionately and unemployment starts to rise generating increased welfare payments and activity measures to try to get people back into work. Thus the budgetary position worsens on both the revenue and expenditure sides of the account. The reverse happens in an expansion and a ‘sustainable’ fiscal system should be able to go through the cycle without the need to change tax rate or expenditure rules.



The key element in this discussion will therefore be the ‘automatic’ stabilisers.2 Automatic in inverted commas because this includes the normal response of governments, which as we have noted is often asymmetric, differing between upturns and downturns – we explore this asymmetry in detail in Chapter 9. We should not regard coordination through automatic stabilisation in this sense as being necessarily a favourable response (Blanchard, 2000) as this reaction is appropriate to demand shocks. Supply shocks can require quite the opposite response. Fiscal policy coordination in the sense we are describing has certain requirements for it to take full effect:

the most important element that has to be sorted out is a reasonably accurate decomposition of the key variables into their ‘cyclical’ and structural components (Brandner et al., 1998). Lastly the incentive structures must be adequate. If there is little to be gained from coordination but substantial costs (both economic and political) in precommitting to do so then coordination is less likely. Similarly if there are no adequate penalties for reneging the incentive to free ride on the system will be substantial. Given that is known, again countries will not cooperate. To assess the importance of policy coordination for policy effectiveness we use the NiGEM multicountry model to compare the effects of different fiscal policy actions in the single country setting and in the case of collective policy action.3 In the simulations (see Table 7.3) public consumption was first increased in all EU countries in an uncoordinated way (i.e. country-by-country). Then it was increased in all EMU countries at the same time and by the same amount (1 per cent).4 In all cases the coordinated fiscal expansion produces almost twice as much an increase in output as an uncoordinated fiscal expansion. (In Table 7.3 the insertion of the letter c in the variable name shows the results of the coordinated action, with the exact definitions of the variables shown in the footnote to the Table.) As expected we have the result that in uncoordinated actions small countries are able to achieve relatively little (mainly because of import leakage). The multiplier values (the last two columns in Table 7.3) reveal that in an uncoordinated case fiscal policy effects for the small countries are mainly only around 0.5. For large countries, the values exceed unity but not by very much. The average value for all countries is 0.72 (with four lags) and 0.63 (with eight lags), 0.85 being the average maximum value. In the case of coordinated policies, there is not much difference

3 In evaluating the effects of fiscal policy, an obvious analytical framework is provided by (structural) vector autoregression (VAR) models (see Blanchard and Perotti (1999), Dalsgaard and De Serres (1999) and Virén (2000a)). Because we concentrate here on the policy coordination problem, structural multicountry models are, however, more convenient. The model vintage used was 2002. 4 The share of public consumption in GDP differs somewhat across EU countries, and so the corresponding GDP effects also differ. The differences in the public consumption/GDP ratio are after all not so large as the following 1998 values indicate: Austria 18.7 per cent, Belgium 21.1 per cent, Denmark 25.5 per cent, Finland 21.4 per cent, France 24.2 per cent, Germany 19.0 per cent, Greece 14.8 per cent, Ireland 13.4 per cent, Italy 18.8 per cent, Luxembourg 14.0 per cent, Netherlands 13.6 per cent, Portugal 20.2 per cent, Spain 15.8 per cent, Sweden 25.9 per cent and UK 18.2 per cent.




Asymmetry and the Role of the Public Sector 169 Table 7.3

A summary of the public consumption simulation y4

y8

yc4

yc8

ymax

ycmax

def

defc

ym

ymc

Austria Belgium Finland France Germany Ireland Italy Netherlands Portugal Spain

0.059 0.099 0.124 0.273 0.224 0.065 0.147 0.107 0.092 0.166

0.042 0.074 0.151 0.261 0.156 0.054 0.128 0.090 0.076 0.159

0.162 0.233 0.175 0.333 0.304 0.232 0.208 0.211 0.156 0.246

0.143 0.208 0.228 0.332 0.224 0.189 0.189 0.195 0.157 0.274

0.107 0.113 0.159 0.274 0.299 0.066 0.156 0.121 0.116 0.175

0.279 0.239 0.268 0.339 0.374 0.233 0.212 0.219 0.241 0.274

–0.154 –0.220 –0.117 –0.168 –0.167 –0.127 –0.146 –0.230 –0.185 –0.157

–0.075 –0.107 –0.050 –0.144 –0.130 –0.079 –0.102 –0.144 –0.144 –0.109

0.574 0.536 0.741 1.130 1.574 0.488 0.829 0.891 0.574 1.109

1.489 1.131 1.251 1.398 1.967 1.740 1.128 1.612 1.193 1.732

Average

0.136 0.119 0.226

0.214

0.159

0.268

–0.167 –0.108 0.845 1.464

between small and large countries. Thus, the average value is 1.25 (with four lags) and 1.17 (with eight lags), 1.46 being again the average maximum value. This represents an improvement for all countries but a major one for the smaller countries. The multiplier values (in the coordination case) are, in fact, quite close to the values obtained by Cohen and Follette (1999) with the US FRB/US macroeconomic model.5 On the other hand, they are a bit higher than the SVAR values obtained by Blanchard and Perotti (1999), which are about one. The multiplier values in the uncoordinated case are, of course very low (suggesting that the marginal propensity to spend out of income is very low and the income elasticity of imports is very high) but also in the case of coordinated fiscal policies the multipliers are not terribly high although they obviously still facilitate fiscal policies. Note also that in the case of uncoordinated policies, the output effect diminishes more rapidly than in the case of coordinated policies. 5

The Cohen and Follette (1999) value with US data (with four lags) was 1.23 which may be compared with our average EMU10 value of 1.25. When the tax rates were set to zero in the FRB/US model the multiplier increased to 1.35 which indicates how much (or, in fact, little) automatic stabilisers will affect on the multiplier. An interesting thing is that the multiplier value of 1.25 implies a relatively low value of the marginal propensity to consume. Assuming the average tax rate to be 0.4 we end up with a marginal propensity to consume to be about 0.3 only (or, 0.4 if we account for imports).



y4 (y8) denotes the output effect of an uncoordinated increase in public consumption (by 1 per cent) after four (eight) quarters, y4c and y8c denote the corresponding values in a case where all countries increase public consumption by the same amount, ymax and ycmax denote the maximum values of y over 20 quarters and ym and ymc the corresponding multiplier values for an increase in public consumption by 1 per cent of GDP. Def and defc denote the deficit effects of an increase in public consumption computed after 20 quarters.


N et he rla nd Po s rtu ga l Sp ai n

G er m an y Ire la nd Ita ly

Be lg iu m Fi nl an d Fr an ce

Au

st ria

Figure 7.1 Long-run effect of a 1 per cent increase in public consumption on government surplus/GDP with and without policy coordination

0

–0,05

–0,1

–0,15

–0,2 coordination

The effect of an increase in public consumption on government deficits is almost equally clear (see Figure 7.1). Deficits increase but because output also increases the effect on the deficit/GDP ratio differs from the pure deficit effect. The values for various countries are surprisingly different, reflecting the differences in the output effects. In other respects, it is rather difficult to say why the country results are so different (the size of the country and the size of the public sector do not seem to explain the size of the output and deficit effects). In these short-run simulations it is perhaps reasonable to ignore the long-term solvency constraint but, not surprisingly, imposing the solvency condition makes a lot of difference, particularly in the long run (when the additional taxes start to have an effect). Thus, the GDP effect almost completely vanishes and the effect on deficits is also quite marginal. If countries increase public consumption and balance the budget in the long run by raising taxes, the long-run output effect is simply zero or even negative.6 Gains from coordination seem to be much larger for small 6

The importance of the solvency condition obviously depends on the level of debt in the country concerned. Given the fact that indebtedness still varies a great deal among the EU countries, we again face an aggregation problem in pursuing EU fiscal policies (see Mayes and Virén (2001) for more about this problem in terms of monetary policy).



uncoordination –0,25

countries while the impact of the solvency requirement depends mainly on the size and nature of the fiscal policy effect. So far, we have considered public consumption only but the picture for direct taxes is very similar. Coordination makes a lot of difference in terms of output effects but the results are less clear for the deficit/ GDP ratio. The problem stems from the output effects. When taxes are increased, output and income decrease, which eliminates part of tax revenues and – ceteris paribus – increases the deficit/GDP ratio because of lower output. If taxes are increased (by 1 per cent) in all EMU member countries at the same time, Finland’s GDP would fall by almost 0.5 per cent and that would also lead to a smaller surplus/GDP ratio. The long-run effect of direct taxes (on output) is noticeably larger than the effect of public consumption. This mainly reflects the larger GDP share of taxes compared with public consumption.7 The dynamics of the effects are, however, quite different, as can be seen from Figure 7.2, which illustrates the effects for the whole EMU area. The effect of public consumption diminishes over time while the tax effect shows no signs of a diminished impact. Figure 7.2

Comparison of expansive fiscal policy effects in the euro area

0,4 0,35 0,3 0,25 0,2 0,15 0,1

public consumption taxes

0,05 0 2000Q1

2001Q1

2002Q1

2003Q1

2004Q1

7 In Finland, for instance, the share of public consumption in GDP was 20.7 per cent in the first quarter of 2000 while the share of direct taxes was 27.1 per cent.




When dealing with fiscal policy simulation, an obvious question is what happens to interest rates. The answer provided by the NiGEM model is ‘not very much’. Thus, imposing the inflation targeting assumption for monetary policy produces only a five basis point increase in long rates in the case of coordinated policies. In the case of uncoordinated policies, the result is practically zero (for instance, in the case of Finland, just onetenth of a basis point). The NiGEM model, like most other models, generates the somewhat odd result that interest rates have a strong impact on deficits while deficits have only a very marginal effect on interest rates.8 This latter result is obviously in sharp contrast with all theorising on credibility and peso effects (but not necessarily with empirical evidence; see e.g. Alesina et al. (1992)). The model result only reflects the direct crowding out effect and does not account for direct expectations and portfolio effects. That is clearly a weakness of the model (and of all similar models). The weakness may also be quite crucial with regard to the assessment of policy coordination effects within EU. The implication of these results is interesting. On the one hand it shows that it is the small countries that have most to gain from policy coordination. However, one can reverse the argument and point out that the others have the least to lose if it is small countries that do not coordinate well. Historically coordination among the EU countries has been fairly weak (Virén, 2000b) except among the countries tracking the deutschemark. There will therefore have to be quite a considerable change in behaviour if this is to occur in future. The SGP has only a limited effect on this as limiting the size of deficits is only part of the problem. Indeed it is only when fiscal policy is not coordinated that this is likely to be a problem as such anomalies occur mainly when small countries experience asymmetric shocks. However, in the early steps of fiscal coordination through European Council of Finance Ministers (ECOFIN) under the SGP the member states, particularly those involved in the euro group, have sought to go a little further and recommend general stances for fiscal policy compared with the cycle (relating to the timing of tax cuts, for example). The BEPG are readily criticised for having no compulsion but in many respects this misses the point. It is simply that on the one hand the member states are becoming steadily more concerned with each other’s 8 As with all such models they are regularly updated, often changing their characteristics markedly. Using earlier or later vintages of the model would no doubt change all the magnitudes but our concern here is with the generalised outcome. The benefits of ‘coordination’ mainly accrue to the smaller countries.






policy while on the other they are becoming more closely linked. Thus, even if overt reasoned policy coordination is in short supply, there is likely to be increasing coordination simply by result. Even though many of the processes for coordination in areas such as employment are through the even looser Open Method of Coordination (Hodson and Maher, 2001) nevertheless there has been considerable policy borrowing and a convergence of some areas, particularly in active labour market polices for example (Bienkowski et al., 2008; Sapir, 2006).


8

Monetary policy is probably the most obvious area of policy where behaviour is likely to be asymmetric or at any rate nonlinear. We can see this from four directions. The first is that since the Phillips curve is nonlinear as set out in Chapter 4, monetary policy acting through the IS curve, Chapter 3, will have a different effect depending on whether the output gap is positive or negative. If the output gap is positive then inflation is quite sensitive to the operation of policy. If the output gap is negative then policy is much less effective. Hence, we can expect that movements in interest rates will be more substantial in the face of the threat of deflation than in the case of excess inflation. Clearly the same applies to any other asymmetries in the operation of the economy, presuming that is that monetary policy is attempting to help hold the economy on a smooth track of low inflation and make a contribution to stable sustainable economic growth. Monetary policy needs to be asymmetric to try to offset some of the asymmetries in the economy. There is a second reason for the wish to act more vigorously in the down phase of the cycle, namely that except in special circumstances, nominal interest rates cannot be negative – the zero bound problem. While it is real interest rates that have the effect on the economy, in severe downturns, not only is output likely to fall but so are prices. Thus a zero nominal interest rate becomes a positive real rate, just at a time when the monetary authority might want to see negative rates. In the present crisis both the US and the UK have attempted to get round the zero bound, as did the Japanese in the 1990s by expanding the money supply so as to create the expectation of future inflation. It is still being debated whether this is effective but, whatever the conclusion, the effect is clearly far from symmetric. 174



Monetary Policy

Thirdly, monetary policy targets themselves tend to have a nonlinear element to them. In some cases they are also asymmetric by construction. However, the asymmetry that is often observed tends to be by result rather than by intention (Mayes, 2007). In most countries, whether inflation targeting or not, the principal aim is to keep inflation below some sort of target level. The target may be unstated as in the US or explicit as in the euro area (less than but close to 2 per cent over the medium term). Small fluctuations below this are acceptable but higher inflation and the threat of deflation are fought with vigour. Where there is an explicit inflation target as in the case of the UK, Canada, Australia, New Zealand, Norway, Japan, Czech Republic, Poland and Sweden among many others, then there is either a target band or central value with a symmetric tolerance band round it. Within the target band outcomes are acceptable but outside they are not. While the transition may be smooth and not show a sharp change in policy once the band is expected to be breached there is a clear asymmetry in policy between being inside and outside the target inflation band. In what follows we explore whether monetary policy can be described by two regimes – normal times and when breaching the edge of the band is threatened – or three – normal times, facing excess inflation and facing deflation. We find evidence for three. Lastly there is the problem of asset prices, which have come to the fore strikingly again in the present crisis. Asset prices perform in a highly volatile manner but their effect on inflation is not symmetric between rapid rises and falls. The effect differs between share prices and house prices. It is not clear whether this concern should be described as monetary stability or financial stability as it clearly has an element of both. The area is still hotly debated. The traditional view is that asset prices are an important information variable for monetary and that they play an important role in the shape of the economic cycle and the inflationary process. The contrary view is that asset prices should themselves be a target of policy. Some see this as a part of monetary policy where asset prices should be included in the target rather than having simply some measure of consumer prices (Goodhart, 2001). Others regard asset prices as a target in their own right (Cecchetti et al., 2000). In the latter case, if the objective is to maintain financial stability rather than monetary stability per se then it will be necessary to have an additional instrument of policy that can operate on one or other side of banks’ balance sheets. We only deal with the traditional monetary policy view, although in our analysis we cast some light on the role of asset prices in the economic cycle and the implications this has for monetary policy.



Monetary Policy 175

Perhaps the most important side of this asymmetry in policy is what has been labelled the ‘Greenspan standard’ by Blinder and Reis (2005). The argue that Alan Greespan’s policy when Chairman of the Federal Reserve was to lean against what appeared to be asset price bubbles and advise people of the risks they were taking, but not to burst them, as it was both very difficult to decide what prices are justifiable and important to avoid being responsible for an unnecessary downturn. It was better in this view to stand ready to act very swiftly and forcefully when the bubble burst to stop the economy entering a downward debt-deflation spiral. Thus, while policy would seek to avoid bubbles and particularly recessions, it was more geared to handling the problem of volatility than eliminating it. It is clear that the same view was held by Greenspan’s successor Ben Bernanke. However, the recent crisis is likely to have changed minds and the balance of effort will move strongly towards avoiding future such drastic cycles. Nevertheless the bulk of this effort will come through work on financial stability rather than from monetary policy. It remains to be seen whether US monetary policy will be more cautious when asset prices next start to rise rapidly. There is no evidence in the rhetoric from the Eurosystem that they have a similar asymmetric approach to asset prices. However, our research and work by Taylor (2009) for example, suggest that policy settings during recent years could be consistent with an implicit view of that form. The rest of this chapter is structured in two parts to cover this whole range of issues. The first deals with the issue of whether monetary policy appears to have been asymmetric while the second considers the role of asset prices in creating asymmetry in the economy and in monetary policy.

8.1

Asymmetry in monetary policy

As explained in earlier chapters, we have set up a simple and very conventional model of the economy, consisting of an IS curve, a Phillips curve, and Okun curve, where these three relationships have the general form ∇yt = a0 + a1∇yt–1 + a2∇yt–2 + a3rrt–i + a4re t–j + a5∇y* t–k

(8.1)

where ∇y is the deviation of output y from its Hodrick–Prescott filtered trend, rr is the real three-month interest rate (i.e. the nominal rate of interest r less the annual rate of consumer price inflation ∇p), re the real exchange rate with the US dollar (in logs) and ∇y* the deviation of




Monetary Policy 177

OECD output from its HP trend. (Lag lengths i, j and k typically vary from one to three quarters in estimation) (see Chapter 3). ∆p = b0 + b1∆pt–1 + b2∆pe + b3∆p* + b4u

(8.2)

pe is expected inflation, p* is the foreign price (in domestic currency) and u is the deviation of unemployment from its trend1 (see Chapter 4) and (8.3)

where pop is the population of working age (see Chapter 6). One of the difficulties about measuring the three foregoing relationships is that in practice the observations that we have are ‘policy inclusive’. Over the period covered by our data, governments have sought to stabilise the economic cycle with some combination of monetary and fiscal policy, partly through ‘automatic stabilisers’ and partly through discretionary action on each occasion. The immediate consequence is that our estimates of these equations may be biased, although we have used estimation methods that should take omitted endogeneity into account. Laxton et al. (1993) argue, for example, that this omission tend to reduce our ability to observe the curvature of the Phillips curve. Not only does the policy reaction reduce the variance but it makes the impact of the underlying relationship appear smaller. However, the impact of policy could be even more distorting if policy is itself not symmetric or linear, as it may not get picked up by the instruments used in the estimation. Economists typically express loss functions in quadratic terms implying that policy will respond more than proportionately as expected outcomes deviate from their targets. However, they tend to make them symmetric (Taylor, 1993). It is perhaps a little more realistic to consider the ‘opportunistic’ approach to policy (Orphanides and Wilcox, 1996) where ‘favourable’ outcomes such as more rapid recoveries, balance of payments improvements etc. than expected are accepted and not offset, whereas less favourable outcomes stimulate further policy responses.2 A more general asymmetric loss function is used in Koskela and Virén (1990) and Virén (1993) drawing on the work of

1

We used a more complex lag structure in estimation. Monetary authorities may seek to offset the asymmetries in the inflationary process, while governments may be more concerned to combat high unemployment or take advantage of periods of higher growth (the ‘inflation bias’ discussed clearly in Walsh (1995) inter alia). 2



∆U = c0 + c1 ∆y + c2 ∆pop

Waud (1970) and Hosomatsu (1970). This also applies to the threshold model approach that we use in this book. However, here we experiment by introducing a policy reaction function directly into the model. It is difficult to decide on a form for the monetary policy reaction function as the EU countries were following different regimes during the period since 1970 to which our data refer. The Bundesbank used a form of enhanced money targeting (Issing et al., 2001), many of the other central banks were targeting the exchange rate first of all within the snake and then the ERM, while others including the UK, Sweden, Finland and Spain have had periods of inflation targeting in the years since the early 1990s. However, as Collins and Siklos (2002) demonstrate, a simple Taylor rule where interest rate smoothing is included provides a reasonable representation of the behaviour of most modern regimes including the US, despite the fact that their ostensible objectives are different. It even embraces the ‘speed limit’ interpretation of US policy (Walsh, 2001; Woodford, 2001), although for some small open economies it might make sense to include the exchange rate. What is particularly interesting is that even though monetary policy is firmly forward-looking in the eyes of central banks including forecasts of inflation and the output gap it does not alter the performance markedly. A simple form for such a Taylor rule would be rt = ρrt–1 + (1 – ρ)[d0 + d1(∆p – ∆pT )t + d2∇yt ]

(8.4)

where the parameter ρ permits an element of interest rate smoothing and ∆pT is the target for inflation (Huang et al., 2001). (∆p – ∆pT ) is expressed as π, inflation, in much of what follows. Where the target remains constant this acts simply as a change in numeraire but the impact is more complicated. Table 8.1 offers a comprehensive set of results for Taylor rules of the form of (8.4), both in Europe and the US for the period between 1971 and the end of 2008, using our dataset drawn from OECD data, relating to the ‘EU 15’ – i.e. the EU between the 1995 enlargement and the 2004 enlargement (less Luxembourg and plus Norway) and in selected cases to the US. It is immediately clear that whether the GDP deflator or the CPI are used a Taylor rule is a reasonable representation of behaviour, irrespective of the estimation method and detailed specification. The only occasion where there does not appear to be the expected relationship is if we look at the euro area countries, during the period that the euro area was operating. If we look at the non-euro area EU countries during the same period then there is still a significant





6.147 (50.55) 5.621 (45.25) 5.698 (54.75) 0.482 (6.21) 0.390 (6.99) 0.320 (4.48) 0.357 (5.35) 0.516 (6.60) 1.061 (5.12) 0.461 (6.69) 0.513 (4.67) 0.258 (1.70) 0.210 (1.12) 1.227 (2.36)

1971–2008 DEF, GAP 1971–2008 CPI, GAP 1971–2008 CPI, GAP 1971–2008 CPI, GAP 1971–2008 CPI, GAP 1991–2008 CPI-H, GAP 1987–2008 CPI, GAP 1971–2008 CPI, GAP 1999–2008 CPI, GAP EMU 1999–2008 CPI, GAP NEMU 1999–2008 CPI, GAP US 1971–2008 CPI, GAP US 1971–2008 CPI, GAP US 1999–2008 CPI, GAP –0.109 (0.63)

0.045 (1.58) 0.015 (0.66) 0.127 (3.11) 0.123 (2.42)

0.477 (19.57) 0.539 (20.30) 0.562 (27.56) 0.067 (5.76) 0.067 (6.46) 0.057 (5.61) 0.100 (4.61)

Inflation

0.182 (1.8)

–0.008 (0.11)

Inflation π

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