Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-06T04:05:10.306Z Has data issue: false hasContentIssue false
  • English
  • Français

CURRENCY UNION, FREE-TRADE AREAS, AND BUSINESS CYCLE SYNCHRONIZATION

Published online by Cambridge University Press:  14 February 2013

Pierangelo De Pace
Pierangelo De Pace*
Affiliation:
Pomona College
*
Address correspondence to: Pierangelo De Pace, Pomona College, Department of Economics; Carnegie Building, Room 205, 425 N College Avenue, Claremont, CA 91711, USA; e-mail: [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

Since the 1970s, the characteristics of international business cycles have changed, and deeper economic integration has modified the features of cross-country comovement. We formally test for correlation shifts in measures of real economic activity and economic/financial integration. In Europe we find some statistically significant evidence of higher correlations for several subgroups of countries following the creation of the EMU in 1999. We detect significantly more pronounced correlations between Mexico and the United States and between Mexico and Canada in North America after the enforcement of the NAFTA in 1994. Results are derived from an econometric framework based on nonparametric iterated stationary bootstrap methods, whose statistical reliability and performance we assess through Monte Carlo simulations.

Keywords

Cycle SynchronizationHypothesis TestingBootstrap Methods
Type
Articles
Information
Macroeconomic Dynamics , Volume 17 , Issue 3 , April 2013 , pp. 646 - 680
Copyright
Copyright © Cambridge University Press 2013

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Agresti, Anna Maria and Mojon, Benoit (2001) Some Stylised Facts on the Euro Area Business Cycle. European Central Bank working paper series WP 95.CrossRefGoogle Scholar
Ahumada, Hildegart and Garegnani, Maria Lorena (2000) Assessing HP filter performance for Argentina and U.S. macro aggregates. Journal of Applied Economics 3, 257–284.CrossRefGoogle Scholar
Artis, Michael (2003) Analysis of European and UK Business Cycles and Shocks. HM Treasury, EMU studies series.Google Scholar
Artis, Michael, Krolzig, Hans-Martin and Toro, Juan (2004) The European business cycle. Oxford Economic Papers 56, 1–44.CrossRefGoogle Scholar
Artis, Michael and Zhang, Wenda (1997a) International business cycles and the ERM: Is there a European business cycle? International Journal of Finance and Economics 2, 1–16.3.0.CO;2-7>CrossRefGoogle Scholar
Artis, Michael and Zhang, Wenda (1997b) Further evidence on the international business cycle and the ERM: Is there a European business cycle? Oxford Economic Papers 51, 120–132.CrossRefGoogle Scholar
Baldwin, Richard E. (2006) In or Out: Does It Matter? An Evidence-Based Analysis of the Euro's Trade Effects. London: Centre for Economic Policy Research.Google ScholarPubMed
Ball, Laurence and Moffitt, Robert (2001) Productivity Growth and the Phillips Curve. Working paper 8421, National Bureau of Economic Research.CrossRefGoogle Scholar
Bayoumi, Tamim and Eichengreen, Barry (1992) Shocking Aspects of European Monetary Unification. Working paper 3949, National Bureau of Economic Research.CrossRefGoogle Scholar
Bejan, Maria (2007) Some Business Cycle Consequences of Trade Agreements: The Case of the North American Free Trade Agreement. RSCAS working papers 2007/03.CrossRefGoogle Scholar
Boone, Laurence (2000) Comparing Semi-structural Methods to Estimate Unobserved Variables: The HPMV and Kalman Filters Approaches. OECD economics department working paper 240.Google Scholar
Bordo, Michael D. and Helbling, Thomas (2003) Have national business cycles become more synchronized? JCMS: Journal of Common Market Studies 40, 23–42.Google Scholar
Camacho, Maximo Gabriel Perez-Quiros, and Saiz, Lorena (2005) Do European business cycles look like one? Journal of Economic Dynamics and Control 32, 2165–2190.CrossRefGoogle Scholar
Clarida, Richard and Gali, Jordi (1994) Sources of real exchange-rate fluctuations: How important are nominal shocks? Carnegie-Rochester Conference Series on Public Policy 41, 1–56.CrossRefGoogle Scholar
Coe, David T. and Helpman, Elhanan (1995) International R&D spillovers. European Economic Review 39, 859–887.CrossRefGoogle Scholar
De Grauwe, Paul and Mongelli, Francesco Paolo (2005) Endogeneities of Optimum Currency Areas. What Brings Countries Sharing a Single Currency Closer Together? European Central Bank working paper series WP 468.Google Scholar
Del Negro, Marco and Otrok, Christopher (2005) Dynamic Factor Models with Time-Varying Parameters. Mimeo, University of Virginia and Federal Reserve Bank of Atlanta.Google Scholar
DiCiccio, Thomas J., Martin, Michael A. and Young, G. Alastair (1992) Fast and accurate approximate double bootstrap confidence intervals. Biometrika 79, 285–295.CrossRefGoogle Scholar
Doyle, Brian M. and Faust, Jon (2002) An investigation of co-movements among the growth rates of G-7 countries. Federal Reserve Bulletin 88, 427–437.CrossRefGoogle Scholar
Doyle, Brian M. and Faust, Jon (2005) Breaks in the variability and comovement of G-7 economic growth. Review of Economics and Statistics 84, 721–740.CrossRefGoogle Scholar
Dueker, Michael and Wesche, Katrin (2003) European business cycles: New indices and their synchronicity. Economic Inquiry 41, 116–131.CrossRefGoogle Scholar
Efron, Bradley and Tibshirani, Robert J. (1993) An Introduction to the Bootstrap. Boca Raton, FL: Chapman & Hall.CrossRefGoogle Scholar
Fisher, Ronald A. (1915) Frequency distribution of the values of the correlation coefficient in samples of an indefinitely large population. Biometrika 10, 507–521.Google Scholar
Fisher, Ronald A. (1921) On the “probable error” of a coefficient of correlation deduced from a small sample. Metron 1, 3–32.Google Scholar
Frankel, Jeffrey A. and Rose, Andrew K. (1998) The endogeneity of the optimum currency area criteria. Economic Journal 108, 1009–1025.CrossRefGoogle Scholar
Gayen, A.K. (1951) The frequency distribution of the product–moment correlation coefficient in random samples of any size drawn from non-normal universes. Biometrika 38, 219–247.CrossRefGoogle ScholarPubMed
Ghysels, Eric (1988) A study toward a dynamic theory of seasonality for economic time series. Journal of the American Statistical Association 83, 168–172.CrossRefGoogle Scholar
Ghysels, Eric and Perron, Pierre (1996) The effect of linear filters on dynamic time series with structural change. Journal of Econometrics 70, 69–97.CrossRefGoogle Scholar
Hall, Peter (1995) The Bootstrap and Edgeworth Expansion. New York: Springer-Verlag.Google Scholar
Harvey, Andrew C. (1985) Trends and cycles in macroeconomic time series. Journal of Business and Economic Statistics 3, 216–227.Google Scholar
Harvey, Andrew C. and Jaeger, Albert (1993) Detrending, stylized facts and the business cycle. Journal of Applied Econometrics 8, 231–247.CrossRefGoogle Scholar
Hawkins, D.L. (1989) Using U statistics to derive the asymptotic distribution of Fisher's Z statistic. American Statistician 43, 235–237.Google Scholar
Horowitz, Joel L. (2001) The bootstrap. In Heckman, J.J. and Leamer, E.E. (eds.), Handbook of Econometrics, Vol.5, ch. 52, pp. 3159–3228. Elsevier.Google Scholar
Hotelling, Harold (1953) New light on the correlation coefficient and its transforms. Journal of the Royal Statistical Society—Series B 15, 193–225.Google Scholar
Kaiser, Regina and Maravall, Augustin (1999) Estimation of the Business Cycle: A Modified Hodrick–Prescott Filter. Spanish Economic Review 1, 175–206.CrossRefGoogle Scholar
Kaminsky, Graciela L. (2005) International Capital Flows, Financial Stability and Growth. United Nations/DESA working paper 10.Google Scholar
Lahiri, Soumendra N. (1999) Theoretical comparisons of block bootstrap methods. Annals of Statistics 27, 386–404.CrossRefGoogle Scholar
Laxton, Douglas and Tetlow, Robert J. (1992) A Simple Multivariate Filter for the Measurement of Potential Output. Bank of Canada technical report, 59.Google Scholar
Maravall, Augustin and del Rio, Ana (2001) Time Aggregation and the Hodrick–Prescott Filter. Banco de España working paper 108.Google Scholar
Mir, Antonio Matas and Osborn, Denise R. (2004) Seasonal Adjustment and the Detection of Business Cycle Phases. ECB working paper 357.Google Scholar
Politis, Dimitris N. and Romano, Joseph P. (1994) The stationary bootstrap. Journal of the American Statistical Association 89, 1303–1313.CrossRefGoogle Scholar
Qu, Zhongjun and Perron, Pierre (2007) Estimating and testing structural changes in multivariate regressions. Econometrica 75, 459–502.CrossRefGoogle Scholar
Rose, Andrew K. and Engel, Charles (2000) Currency unions and international integration. Journal of Money, Credit and Banking 34, 804–826.Google Scholar
Sims, Christopher A. (1993) Rational expectations modeling with seasonally adjusted data. Journal of Econometrics 55, 9–19.CrossRefGoogle Scholar
Stock, James H. and Watson, Mark W. (2005) Understanding changes in international business cycle dynamics. Journal of the European Economic Association 3, 968–1006.CrossRefGoogle Scholar