Long-run identifying restrictions on VARs within the AS-AD framework

Pentecôte, J.-S. (2010): Long-run identifying restrictions on VARs within the AS-AD framework.

Abstract

Bayoumi and Eichengreen’s (BE, 1994) article has been very influent in the empirics of the core-periphery view of fixed exchange rate agreements. They rely on the basic AS-AD macroeconomic model in order to identify supply and demand shocks through long-run restrictions in vector autoregressions. Doing this should enable one to assess the size of such disturbances and the asymmetry between countries. While reference is usually made to Blanchard and Quah (BQ, 1989), it is shown here how this factorization has been modified by BE and how the two resulting decomposition schemes can be linked. Contrary to BE’s premise, relaxing the assumption of shocks of equal size is not just a matter of scale. The empirical properties of the exchange regime are modified, especially as regards the correlation of shocks. Given the VAR setting used in the related studies, it is also established that zero-constraints on either instantaneous or long-run impulse responses provide identical results. An empirical assessment of the euro currency area over 1996-2008 illustrate these points. The recorded evidence suggests that non-zero restrictions imply slope coefficients of the AS and AD curves close to values derived from New-Keynesian models. b

• Item Type: MPRA Paper
• Language: English
• Keywords: fixed exchange rates, core-periphery, long-run restrictions, structural VARs b
• Subjects: C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E32 - Business Fluctuations ; Cycles; F - International Economics > F3 - International Finance > F33 - International Monetary Arrangements and Institutions

References

Alessi A., Barigozzi M. and Capasso M. (2009). “Nonfundamentalness and identification in structural econometric models: a review”, mimeo, November.

Bayoumi T. and Eichengreen B. (1992). “Shocking aspects of European monetary integration », in F. Torres and F. Giavazzi (eds.), Adjustment and Growth in the European Monetary Union, Cambridge University Press, New York, pp. 193–292.

Bayoumi T. and Eichengreen B. (1994). “Adjustment under Bretton Woods and the Post-Bretton-Woods float: an impulse-response analysis”, Economic Journal, 104(425), 813-27.

Bayoumi T. and Taylor M. (1995). “Macro-economic shocks, the ERM, and tri-polarity”, Review of Economics and Statistics, 77(2), May, 321-31.

Bergman M. (2005). “Do long-run restrictions identify supply and demand disturbances?”, mimeo, October.

Blanchard O. and Quah D. (1989). “The dynamic effects of aggregate demand and supply disturbances”, American Economic Review, 79(4), September, 655-73.

Blanchard O. and Quah D. (1993). “The dynamic effects of aggregate demand and supply disturbances: Reply”, American Economic Review, 83(3), June, 653-8.

Braun P. and Mittnick S. (1993). “Misspecifications in vector autoregressions and their effects on impulse responses and variance decompositions”, Journal of Econometrics, 59, 319-41.

Brissimis S. and Skotida I. (2008). “Optimal monetary policy in the Euro area in the presence of heterogeneity”, Journal of International Money and Finance, 27(2), 209-26.

Canova F. (2009). “How much structure in empirical models?”, chapter 2 in T. Mills and K. Patterson (eds), Palgrave Handbook of Applied Econometrics. Vol. 2, 68-97.

Cooley T. and Dwyer M. (1998). “Business cycle analysis without much theory. A look at structural VARs”, Journal of Econometrics, 83, 57-88.

Cooley T. and LeRoy S. (1985). “Atheoretical macroeconometrics. A critique”, Journal of Monetary Economics, 16, 283-308.

Cover J., Enders W. and Hueng C. (2006). “Using the aggregate demand-aggregate supply model to identify structural demand-side and supply-side shocks: results using a bivariate VAR”, Journal of Money, Credit, and Banking, 38(3), April, 777-90.

Crowder J. (1995). “The dynamic effects of aggregate demand and supply disturbances. Another look”, Economics Letters, 49, 231-7.

Faust J. and Leeper E. (1997). “When do long-run identifying restrictions give reliable results?”, Journal of Business & Economic Statistics, 15, July, 345–53.

Fidrmuc, J. and Khoronen I. (2006). “Meta-Analysis of the Business Cycle Correlation between the euro Area and the CEECs”, Journal of Comparative Economics, 34 (3), 518-37.

Fisher L. and Hu H.-S. (1999). “Weak exogeneity, and long-run and contemporaneous identifying restrictions in VEC models”, Economics Letters, 63, 159-65.

Fisher L., Hu H.-S., and Summers P. (2000). “Structural identification of permanent shocks in VEC model: a generalization”, Journal of Macroeconomics, 22(1), 53-68.

Fry R. and Pagan A. (2009). “Sign restrictions in structural vector autoregressions: a critical review”, mimeo, December.

Keating J. (2009). “When do Wold orderings and long-run recursive identifying restrictions yield identical results?”, mimeo, University of Kansas.

Lee J. and Crowley P. (2010). “Evaluating the Monetary Policy of the European Central Bank”, mimeo, Federal Reserve Bank of Atlanta.

Lippi F. and Reichlin L. (1993). “The dynamic effects of aggregate demand and supply disturbances. Comment”, American Economic Review, 83(3), 644-52.

Pagan A. and Pesaran H. (2008). “Econometric analysis of structural systems with permanent and transitory shocks”, Journal of Economic Dynamics and Control, 32, 3376-95.

Quah D. (1995). “Misinterpreting the dynamic effects of aggregate demand and supply disturbances”, Economics Letters, 49, 247-50.

Ribba A. (1997). “A note on the equivalence of long-run and short-run identifying restrictions in cointegrated systems”, Economics Letters, 56, 273-76.

Rubio-Ramirez J., Waggoner D. and Zha T. (2010). “Structural vector autoregressions: theory of identification and algorithms for inference”, Review of Economic Studies, 77, 665-96.