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Tracking the Economy in the Largest Euro Area Countries: a Large Datasets Approach

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Convergence or Divergence in Europe?

Summary

The paper proposes a set of monthly business (growth-) cycle indicators for Germany, France, Italy and the euro area useful for ex post characterization of the cycle, and, most importantly, to assess the current economic outlook. These indicators are projections of quarterly aggregates on the space spanned by a set of regressors extracted from a large panel of monthly series. Being based on static linear combinations of monthly series, they do not suffer from the end-of-sample problem associated with traditional bilateral filters (HP filter). The indicators are used to: (1) study the degree of co-movement and synchronization across economies; (2) derive a dating of the cycle; (3) obtain the ‘stylized’ cyclical facts; (4) assess the predictive content of the panel for GDP growth. The monthly indicators are good forecasters of GDP performing often better than other simple methods. As expected, since the growth cycle indicator is a ‘smoothed’ estimate of the GDP growth, the best forecasts are obtained in terms of year-on-year (rather than quarter-on-quarter) GDP growth.

The authors wish to thank Olivier de Bandt, Sandra Eickmeier, Heinz Herrmann, the discussants and the participants of the conference held in Paris for their helpful comments. Many ideas presented here were first developed in collaboration with Filippo Altissimo, Antonio Bassanetti, Mario Forni, Marco Lippi and Lucrezia Reichlin in a joint research that led to the construction of the euro area business cycle indicator Eurocoin, currently published each month by the CEPR. The usual disclaimer applies.

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References

  • Altissimo, F., Bassanetti, A., Cristadoro, R., Forni, M., Lippi, M., Reichlin, L. and Veronese, G. (2001) A real time coincident indicator for the euro area business cycle, Working paper, CEPR.

    Google Scholar 

  • Altissimo, F., Marchetti, D. and Oneto, G. (2000) The italian business cycle: Coincident and leading indicators and some stylized facts, Temi di discussione, Banca d’Italia.

    Google Scholar 

  • Backus, D. and Kehoe, P. (1992) International evidence on the historical properties of business cycles, American Economic Review 82(4),864–888.

    Google Scholar 

  • Baffigi, A., Golinelli, C. and Parigi, G. (2002) Euro area bridge models, Temi di discussione, Banca d’Italia.

    Google Scholar 

  • Baxter, A. and King, R.G. (1999) Measuring business cycles approximate band-pass filters for economic time series, Review of Economics and Statistics 81(4),575–593.

    Article  Google Scholar 

  • Baxter, M. (1995), International Trade and Business Cycles, p.1801–1864, in Handbook of International Economics, vol.3, Grossman, G and Rogoff, K. editors, North Holland.

    Google Scholar 

  • Bruno, G. and Otranto, E. (2004) Dating the italian business cycle: a comparison of procedures., Working Paper 41, ISAE.

    Google Scholar 

  • Brillinger, D.R. (1981), Time Series Data Analysis and Theory, Holden Day, San Francisco.

    Google Scholar 

  • Bry, G. and Boschan, C. (1971) Cyclical analysis of time series: Selected procedures and computer programs, Technical Working Paper 20, NBER.

    Google Scholar 

  • Burns, A.F. and Mitchell, W.G. 1946, Measuring Business Cycles, NBER, New York.

    Google Scholar 

  • Burnside, C. (1998) Detrending and business cycle facts: a comment, Journal of Monetary Economics 41,513–532.

    Article  Google Scholar 

  • Canova, F. (1994) Detrending and turning points, European Economic Review 38,614–623.

    Article  Google Scholar 

  • Canova, F. (1999) Does detrending matter for the determination of the reference cycle and the selection of turning points, Economic Journal 49,126–149.

    Article  Google Scholar 

  • Christiano, L. and Fitzgerald, J. (2003) The band pass filter, International Economic Review 44,435–465.

    Article  Google Scholar 

  • Cristadoro, R., Forni, M., Reichlin, L. and Veronese, G. (2005) A core inflation indicator for the euro area, Journal of Money Credit and Banking 37(3),539–560.

    Article  Google Scholar 

  • Croux, C.,, Forni, M. and Reichlin, L. (1998) A measure of comovement for economic variables: Theory and empirics, Review of Economics and Statistics 83(2),232–241.

    Article  Google Scholar 

  • Eickmeier, S. (2005) Common stationary and non-stationary factors in the euro area analyzed in large-scale factor model, mimeo, Bundesbank.

    Google Scholar 

  • Forni, M., Hallin, M., Lippi, M. and Reichlin, L. (2005a) The generalized factor model: identification and estimation, The Review of Economics and Statistics 82, 550–554.

    Google Scholar 

  • Forni, M., Hallin, M., Lippi, M. and Reichlin, L. (2005b) The generalized factor model: One-sided estimation and forecasting, Journal of the American Statistical Association.

    Google Scholar 

  • Hodrick, R. and Prescott, E. (1997) Post war us business cycles: an empirical investigation, Journal of Money, Credit and Banking 29,1–16.

    Article  Google Scholar 

  • Kaiser, R. and Maravall, A. 2001, Measuring Business Cycles In Economic Time Series, Springer Verlag.

    Google Scholar 

  • Kydland, F. and Prescott, E. (1982) Time to build and aggregate fluctuations, Econometrica 50,1345–1370.

    Article  MATH  Google Scholar 

  • Lucas, R.E. (1977) understanding Business Cycles, Carnegie-Rochester Conference Series on Public Policy 5,7–30.

    Article  Google Scholar 

  • Matheron, J. (2004) Business Cyle Datation in France, Germany and Italy, mimeo, Banque de France.

    Google Scholar 

  • Monch, E. and Uhlig, H. (1999) Towards a monthly business cycle chronology for the euro area, mimeo, Humboldt University, Berlin.

    Google Scholar 

  • Nelson, C. and Plosser, C. (1982) Trends and random walks in macroeconomic time series, Journal of Monetary Economics 10, 139–162.

    Article  Google Scholar 

  • Obstfeldt, M. and Rogoff, K. (2000) The six major puzzles of international macroeconomics: is there a common cause?, In: NBER Macro Annual, MIT Press

    Google Scholar 

  • Harding, D. and Pagan, A. (2001) Rejoinder to James Hamilton, mimeo, Australian National University.

    Google Scholar 

  • Harding, D. and Pagan, A. (2002a) Dissecting the cycle: a methodological investigation, Journal of Monetary Economics 49, 365–381.

    Article  Google Scholar 

  • Harding, D. and Pagan, A. (2002b) Synchronization of cycles, Working paper, Australian National University.

    Google Scholar 

  • Monch E. and Uhlig, H. (2004), Towards a Monthly Business Cycle Chronology for the Euro Area.

    Google Scholar 

  • Ravn, M. and Uhlig, H. (2001) On adjusting the hp-filter for the frequency of observations, Discussion Paper 40, CEPR.

    Google Scholar 

  • Sargent, T., 1987, Macroeconomic Theory, 2nd Edition, Academic Press, London.

    MATH  Google Scholar 

  • Stock, J. and Watson, M. (1989) New Indexes of Coincident and Leading Economic Indicators, in NBER Macroeconomics Annual.

    Google Scholar 

  • Stock, J. and Watson, M. (1999) Business Cycle Fluctuations in U.S. Macroeconomic Time Series, Handbook ofMacroeconomics, Vol. 1A, North Holland, 3–64.

    Google Scholar 

  • Stock, J. and Watson, M. (2002) Macroeconomic forecasting using diffusion indexes, Journal of Business and Economic Statistics 20(2), 147–162.

    Article  MathSciNet  Google Scholar 

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Cristadoro, R., Veronese, G. (2006). Tracking the Economy in the Largest Euro Area Countries: a Large Datasets Approach. In: Convergence or Divergence in Europe?. Springer, Berlin, Heidelberg . https://doi.org/10.1007/3-540-32611-1_6

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