Jump to ContentJump to Main Navigation
Show Summary Details
More options …

The B.E. Journal of Macroeconomics

Editor-in-Chief: Cavalcanti, Tiago / Kambourov, Gueorgui

Ed. by Abraham, Arpad / Carceles-Poveda , Eva / Debortoli, Davide / Lambertini, Luisa / Nimark, Kristoffer / Wang, Pengfei

2 Issues per year


IMPACT FACTOR 2016: 0.043
5-year IMPACT FACTOR: 0.376

CiteScore 2016: 0.36

SCImago Journal Rank (SJR) 2016: 0.312
Source Normalized Impact per Paper (SNIP) 2016: 0.272

Online
ISSN
1935-1690
See all formats and pricing
More options …

How have global shocks impacted the real effective exchange rates of individual euro area countries since the euro’s creation?

Matthieu Bussiere / Alexander Chudik / Arnaud Mehl
Published Online: 2013-04-02 | DOI: https://doi.org/10.1515/bejm-2012-0068

Abstract

This paper uncovers the response pattern to global shocks of euro area countries’ real effective exchange rates before and after the start of Economic and Monetary Union (EMU), a largely open ended question when the euro was created. We apply to that end a newly developed methodology based on high dimensional VAR theory. This approach features a dominant unit to a large set of over 60 countries’ real effective exchange rates and is based on the comparison of two estimated systems: one before and one after EMU. We find strong evidence that the pattern of responses depends crucially on the nature of global shocks. In particular, post-EMU responses to global US dollar shocks have become similar to Germany’s response before EMU, i.e., to that of the economy that used to issue Europe’s most credible legacy currency. By contrast, post-EMU responses of euro area countries to global risk aversion shocks have become similar to those of Italy, Portugal or Spain before EMU, i.e., of economies of the euro area’s periphery. Our findings also suggest that the divergence in external competitiveness among euro area countries over the last decade, which is at the core of today’s debate on the future of the euro area, is more likely due to country-specific shocks than to global shocks.

Keywords: euro; high-dimensional var; identification of shocks; real effective exchange rates; weak and strong cross sectional dependence; JEL Classification: C21, C23

References

  • Adrian, T., E. Etula, and H. S. Shin (2010). Risk Appetite and Exchange Rates. FRB of New York Staff Report No. 361.Google Scholar

  • Andrews, D. W. K. and W. Ploberger (1994). Optimal Tests When a Nuisance Parameter is Present only Under the Alternative. Econometrica, 62(6):1383–414.CrossrefGoogle Scholar

  • Artis, M. J., D. Osborn, and P. J. Perez (2006). The International Business Cycle in a Changing World: Volatility and The Propagation of Shocks in the G-7. Open Economies Review, 17(3):255–79.Google Scholar

  • Baldwin, R. (2006). The Euro’s Trade Effect. ECB Working Paper No. 594.Google Scholar

  • Berger, H. and V. Nitsch (2010). The Euro’s Effect on Trade Imbalances. IMF Working Paper 10/226.Google Scholar

  • Blanchard, O. and G. M. Milesi-Ferretti (2009). Global Imbalances: In Midstream? IMF Staff Position Note SPN/09/29, International Monetary Fund.Google Scholar

  • Canova, F., M. Ciccarelli, and E. Ortega (2006). Similarities and Convergence of G-7 Cycles. Journal of Monetary Economics, 54(3):850–78.Google Scholar

  • Cappiello, L., A. Kadareja, and S. Manganelli (2009). The Impact of the Euro on Equity Markets: A Country and Sector Decomposition. Journal of Financial and Quantitative Analysis, 45(2):473–502.Google Scholar

  • Cecchetti, S. G., A. Flores-Lagunes, and S. Krause (2005). Assessing The Sources of Changes in The Volatility of Real Growth: Rba Annual Conference Volume. In C. Kent, and D. Norman (eds.), The Changing Nature of the Business Cycle. Sydney: Reserve Bank of Australia, pp. 115–38.Google Scholar

  • Chudik, A. and M. H. Pesaran (2011). Infinite Dimensional VARs and Factor Models. Journal of Econometrics, 163:4–22.CrossrefWeb of ScienceGoogle Scholar

  • Chudik, A. and M. H. Pesaran (2013). Econometric Analysis of High Dimensional VARs Featuring a Dominant Unit. Econometric Reviews, 32:592–649.Web of ScienceCrossrefGoogle Scholar

  • Chudik, A., M. H. Pesaran, and E. Tosetti (2011). Weak and Strong Cross Section Dependence and Estimation of Large Panels. Econometrics Journal, 14:C45–C90.Web of ScienceCrossrefGoogle Scholar

  • Coeurdacier, N. and P. Martin (2009). The Geography of Asset Trade and The Euro: Insiders and Outsiders. Journal of the Japanese and International Economies, 23(2):90–113.CrossrefWeb of ScienceGoogle Scholar

  • Dees, S., M. H. Pesaran, L. V. Smith, and R. P. Smith (2010). Supply, Demand and Monetary Policy Shocks in a Multi-country New Keynesian Model. CESifo Working Paper Series No. 3081.Google Scholar

  • DeSantis, R. and B. Gerard (2009). International Portfolio Reallocation: Diversification benefits and European Monetary Union. European Economic Review, 53:1010–27.CrossrefWeb of ScienceGoogle Scholar

  • Dées, S., F. di Mauro, M. H. Pesaran, and L. V. Smith (2007). Exploring the International Linkages of the Euro Area: A Global VAR Analysis. Journal of Applied Econometrics, 22:1–38.CrossrefGoogle Scholar

  • Enders, Z., P. Jung, and G. J. Müller (2009). Has the Euro Changed the Business Cycle? University of Bonn Discussion Paper, 06/2009.Google Scholar

  • Engel, C. and J. H. Rogers (2002). European Product Market Integration After the Euro. Economic Policy, 19:347–84.Google Scholar

  • ESCB (2012). Competitiveness and External Imbalances within the Euro Area. ECB Occasional Paper No. 139.Google Scholar

  • European Central Bank (2008). Monthly Bulletin – 10th Anniversary of the ECB. European Central Bank.Google Scholar

  • European Commission (2008). EMU@10: Successes and Challenges after 10Years of Economic and Monetary Union. European Commission. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee, the Committee of the Regions and the European Central Bank.Google Scholar

  • Giavazzi, F. and L. Spaventa (2010). Why the Current Account Matters in a Monetary Union: Lessons from the Financial Crisis in the Euro Area. CEPR Discussion Papers 8008.Google Scholar

  • Giavazzi, F., A. Giovannini, D. Begg, and L. Katseli (1986). The EMS and the Dollar. Economic Policy, 1(2):455–85.CrossrefGoogle Scholar

  • Goldberg, L. S. (2010). Is the International Role of the Dollar Changing? Current Issues in Economics and Finance, 16(1):1–7. Federal Reserve Bank of New York.Google Scholar

  • Hausman, J. A. (1978). Specification Tests in Econometrics. Econometrica, 46:251–72.Google Scholar

  • Holly, S., M. H. Pesaran, and T. Yamagata (2010). Spatial and Temporal Diffusion of House Prices in the UK. CESifo Working Paper No. 2913.Google Scholar

  • International Monetary Fund (2005, September). World Economic Outlook. International Monetary Fund.Google Scholar

  • International Monetary Fund (2008). Dollar Depreciation and Commodity Prices. International Monetary Fund. World Economic Outlook, 48–50.Google Scholar

  • Jaumotte, F. and P. Sodsriwiboon (2010). Current Account Imbalances in the Southern Euro Area. IMF Working Papers 10/139.Google Scholar

  • Koop, G., M. H. Pesaran, and S. M. Potter (1996). Impulse Response Analysis in Nonlinear Multivariate Models. Journal of Econometrics, 74:119–47.CrossrefGoogle Scholar

  • Lane, P. (2006). Global Bond Portfolios and EMU. International Journal of Central Banking, 2(2):1–23.Google Scholar

  • McCauley, R. N. and P. McGuire (2009). Dollar Appreciation in 2008: Safe Haven, Carry Trades, Dollar Shortage and Overhedging. BIS Quarterly Review, 85–93.Google Scholar

  • Mihov, I. (2001). Monetary Policy Implementation and Transmission in the European Monetary Union. Economic Policy, 16:369–406.CrossrefGoogle Scholar

  • Nyblom, J. (1989). Testing for the Constancy of Parameters Over Time. Journal of the American Statistical Association, 84:223–30.CrossrefGoogle Scholar

  • Obstfeld, M. and K. Rogoff (2005). Global Current Account Imbalances and Exchange Rate Adjustments. Brookings Papers on Economic Activity, 1:67–123.CrossrefGoogle Scholar

  • Obstfeld, M. and K. Rogoff (2009). Global Imbalances and the Financial Crisis: Products of Common Causes. CEPR Discussion Papers No. 7606.Google Scholar

  • Pesaran, M. H. (2006). Estimation and Inference in Large Heterogenous Panels with Multifactor Error Structure. Econometrica, 74:967–1012.CrossrefGoogle Scholar

  • Pesaran, M. H. and Y. Shin (1998). Generalized Impulse Response Analysis in Linear Multivariate Models. Economics Letters, 58:17–29.CrossrefGoogle Scholar

  • Pesaran, M. H., T. Schuermann, and S. Weiner (2004). Modelling Regional Interdependencies Using a Global Error-Correcting Macroeconometric Model. Journal of Business and Economics Statistics, 22:129–62.CrossrefGoogle Scholar

  • Ploberger, W. and W. Krämer (1992). The CUSUM Test with OLS Residuals. Econometrica, 60(2):271–86.CrossrefGoogle Scholar

  • Popescu, A. and F. Smets (2010). Uncertainty, Risk-Taking, and The Business Cycle in Germany. CESifo Economic Studies, 56:596–626.CrossrefWeb of ScienceGoogle Scholar

  • Quandt, R. E. (1960). Tests of the Hypothesis That a Linear Regression System Obeys Two Separate Regime. Journal of the American Statistical Association, 55:324–30.CrossrefGoogle Scholar

  • Rogers, J. (2007). Monetary Union, Price Level Convergence, and Inflation: How Close is Europe to the USA? Journal of Monetary Economics, 53(3):785–96.CrossrefGoogle Scholar

  • Stock, J. H. and M. W. Watson (2002). Has the Business Cycle Changed and Why? NBER Working Paper No. 9127.Web of ScienceGoogle Scholar

  • Taylor, M., D. Peel, and L. Sarno (2001). Nonlinear Mean-Reversion in Real Exchange Rates: Toward a Solution to the Purchasing Power Parity puzzles. International Economic Review, 42(4):1015–42.CrossrefGoogle Scholar

  • Wolf, M. (2010, September). Germans are Wrong: the Eurozone is Good for Them. Financial Times.Google Scholar

  • Wu, D. M. (1973). Alternative Tests of Independence Between Stochastic Regressors and Disturbances. Econometrica, 41:733–50.CrossrefGoogle Scholar

About the article

Corresponding author: Matthieu Bussiere, Banque de France – International Macroeconomics Division, Banque de France 49-1374 DERIE-SEMSI Paris 75049, France, e-mail:


Published Online: 2013-04-02

Published in Print: 2013-01-01


In particular, the euro area Finance Ministers noted in early-2010 that “competitiveness divergences and current-account imbalances increased steadily in pre-crisis years and have in most cases largely persisted throughout the crisis […] Given vulnerabilities and the magnitude of the adjustment required, the need for policy action is particularly pressing in Member States showing persistently large current-account deficits and large competitiveness losses” (Eurogroup conclusions on the surveillance of intra-euro area competitiveness and macroeconomic imbalances, Brussels, 15 March 2010).

European Commission (2008) and European Central Bank (2008) provide thorough surveys.

As to the former aspect, it is important to note that there is heterogeneity among euro area countries in terms of exposure to both overall non-euro area trade and to the various countries and regions outside the euro area.

For instance, for a VAR model with three lags, we have in our case 62 (number of variables) ×62×3=11532 parameters to estimate and only 12 (months) ×10 (years) ×62=7440 observations.

Arguably, the aggregated impact of non-neighbors could still be large, depending on the degree of cross-section dependence among the units. Such an aggregated impact is in general important when the cross-section dependence is strong [in the sense defined by Chudik, Pesaran, and Tosetti (2011)], in which case it is possible to control for it by using cross-section averages, an idea originally introduced by Pesaran (2006) in the context of the estimation of large heterogenous panels with a multi-factor error structure.

Changes in risk aversion and appetite are regarded as important drivers of foreign exchange markets, not only when it comes to emerging market economies but also, more recently, to advanced economies [e.g., McCauley and McGuire (2009), Adrian, Etula, and Shin (2010)]. For a recent discussion of risk aversion shocks, see also Popescu and Smets (2010).

As to price competitiveness specifically, this study finds that differences appear to have stemmed mainly from higher increases in labor costs in external deficit countries relative to those with external surpluses. In turn, these heterogeneous developments in labor costs mirror heterogeneous developments in tax wedges (i.e., direct taxes and social security contributions). According to this report, slow productivity growth also played a role in some countries, reflecting resource reallocations from traded to non-traded sectors. In addition to this, the report finds that some elements of non-price competitiveness also appear to explain heterogeneous developments in external competitiveness across euro countries in the first 10 years of EMU. These elements include technological innovation (R&D), labor force characteristics (e.g., skills), product market regulations and business environment factors (e.g., procedures for enforcing contracts).

These results are not reported due to space considerations. Even in longer time spans of data, it is common to find unit roots in real effective exchange rates [see e.g., Pesaran, Schuermann, and Weiner (2004)], although for very long periods – such as centuries – there is some evidence of mean reversion, see for instance Taylor, Peel, and Sarno (2001). Such very long datasets are only available for a handful of currencies, however.

On the one hand, specifying a relatively parsimonious model often yields strong estimation results, but at the expense of omission bias of key variables if the model is too simple. On the other hand, a more complex model allows for a richer representation of the interactions between variables, but at the expense of estimation precision, due to the loss in degrees of freedom.

Overal, our set of neighbors is

It is difficult to establish long-run relations for real exchange rates in such a short time span (one decade), as documented by an extensive literature on the relative version of the purchasing power parity.

Our main data source for the real effective exchange rates in our sample is the IMF IFS database, which does not disclose details on the composition of their currency baskets and which might also change over time.

Identifying economic shocks is arguably a traditional challenge in the literature due to, e.g., the potential existence of different competing structural models underlying such shocks, or to the difficulty to identify their geographic origin. As to the latter, it is reasonable indeed to assume that different structural shocks, say productivity and monetary policy shocks, are uncorrelated within a closed economy, but not when other economies are considered in the analysis. This therefore makes it even more difficult to identify such shocks in a large system. For a related discussion, see Dees et al. (2010).

See Chudik and Pesaran (2011) for further details on the analysis of systems featuring a dominant unit.

In particular, the exceptional rise in volatility in the US dollar, euro and yen during the 2007/09 global crisis, has been largely ascribed to an unprecedented rise in risk aversion which triggered a massive flight to the safety and liquidity of US dollar-denominated assets and confounded previous scenarios of disorderly unwinding of global imbalances.

Identification with sign restrictions is referred to as “weak” here in the sense that a variety of structural models could satisfy the selected signs.

We also used alternative sets of restrictions to identify risk shocks, which did not change our main findings qualitatively.

For shocks to risk aversion, we follow the literature in summarizing the available information in multiple structural models by reporting median and quantiles of impulse responses obtained through bootstrap replications. It should be highlighted, however, that the median itself is not an impulse response function per se (and generally does not belong to the space of impulse responses). In the same spirit, quantiles cannot be interpreted as confidence intervals in this case and, for the same reason, measures of euclidian distance cannot be calculated.

To ensure comparability between the two estimation periods, we only include in our sample those countries which were members of the euro area from the outset in 1999 (i.e., Austria, Belgium, Finland, France, Germany, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain) and Greece, which joined relatively early on (namely in 2001). We therefore discard those new EU Member States which joined later (after 2004), including Slovenia, Slovakia, Malta, Cyprus and Estonia.

Prior to EMU, a large part of the volatility in the relative evolution of REERs across European economies occurred between the late 1980s and the mid-1990s.

Over the two decades we consider, some of the countries in our sample have indeed experienced well-known devaluations, exchange rate regime changes and other large country-specific shock (such as Germany’s unification).

Corresponding to an appreciation of the US dollar, Japanese yen and Swiss franc of 0.5%, 1.5% and 1.2%, respectively; a depreciation of the Korean won and Polish zloty of –3.1% and –3.0%, respectively; and an increase in the VIX of 10%.

Corresponding to an appreciation of the US dollar, Japanese yen and Swiss franc of 1.2%, 2.2% and 1.2%, respectively; a depreciation of the Korean won and Polish zloty of –2.4% and –2.2%, respectively; and an increase in the VIX of 20%.

This seemed notably the case in the 2007/9 crisis, in particular at times of heightened uncertainty and flight to the safety and liquidity of US dollar assets [see e.g., McCauley and McGuire (2009)].

Such a view is most candidly expressed by a column of M. Wolf, the Financial Times’ Chief Economics commentator, written in late-2010 (Wolf 2010): “The euro has also shielded the German economy from what would have been still bigger shocks: imagine what would have happened, in the absence of the euro. The exchange rate of the German mark would have exploded upwards, as currency crises savaged the European economy, as happened in the 1990s. In peripheral Europe, currency depreciations would have been at least as big as, if not bigger than, sterling’s. The absence of such shocks has greatly enhanced the prospects for the German recovery. The creation of the eurozone was, for this reason alone, much more than a favour Germany did for its partners. It was also a big economic (not to mention political) gain for Germany. German industrialists are clear on this, as is the government.”


Citation Information: The B.E. Journal of Macroeconomics, Volume 13, Issue 1, Pages 1–48, ISSN (Online) 1935-1690, ISSN (Print) 2194-6116, DOI: https://doi.org/10.1515/bejm-2012-0068.

Export Citation

©2013 by Walter de Gruyter Berlin Boston. Copyright Clearance Center

Citing Articles

Here you can find all Crossref-listed publications in which this article is cited. If you would like to receive automatic email messages as soon as this article is cited in other publications, simply activate the “Citation Alert” on the top of this page.

[1]
Chun Chang, Kaiji Chen, Daniel F. Waggoner, and Tao Zha
NBER Macroeconomics Annual, 2016, Volume 30, Number 1, Page 1
[2]
Panayotis G. Michaelides, Efthymios G. Tsionas, and Konstantinos N. Konstantakis
Journal of Economic Dynamics and Control, 2018
[3]
Goulven Rubin
Journal of the History of Economic Thought, 2016, Volume 38, Number 03, Page 285
[4]
Efthymios G. Tsionas, Konstantinos N. Konstantakis, and Panayotis G. Michaelides
Journal of International Financial Markets, Institutions and Money, 2016, Volume 42, Page 1
[5]
Yuanyuan Chen and Stuart Fowler
Computational Economics, 2016, Volume 48, Number 4, Page 649
[6]
Giulia Bettin, Andrea F. Presbitero, and Nikola L. Spatafora
The World Bank Economic Review, 2015, Page lhv053
[7]
Kazeem Bello Ajide, Ibrahim D. Raheem, and Oluwatosin Adeniyi
International Journal of Development Issues, 2015, Volume 14, Number 3, Page 190
[8]
Alexander Chudik and M. Hashem Pesaran
Journal of Economic Surveys, 2016, Volume 30, Number 1, Page 165

Comments (0)

Please log in or register to comment.
Log in