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Macroeconomic forecasting in the Euro area: Country specific versus area-wide information

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  • Marcellino, Massimiliano
  • Stock, James H.
  • Watson, Mark W.

Abstract

This paper investigates time series methods for forecasting four Euro-area wide aggregate variables: real GDP, industrial production, price inflation, and the unemployment rate. We consider two empirical questions arising from this problem. First, is it better to build aggregate Euro-area wide forecasting models for these variables, or are there gains from aggregating country-specific forecasts for the component country variables? Second, are there gains from using information from additional predictors beyond simple univariate time series forecasts, and if so, how large are these gains, and how are these gains best achieved? It turns out that typically there are gains from forecasting these series at the country level, then pooling the forecasts, relative to forecasting at the aggregate level. This suggests that structural macroeconometric modeling of the Euro area is appropriately done at the country-specific level, rather than directly at the aggregate level. Moreover, our simulated out-of-sample forecast experiment provides little evidence that forecasts from multivariate models are more accurate than forecasts from univariate models. If we restrict attention to multivariate models, the forecasts obtained from a dynamic factor model appear to be somewhat more accurate than the other methods.
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  • Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2003. "Macroeconomic forecasting in the Euro area: Country specific versus area-wide information," European Economic Review, Elsevier, vol. 47(1), pages 1-18, February.
  • Handle: RePEc:eee:eecrev:v:47:y:2003:i:1:p:1-18
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    1. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2000. "Reference Cycles: The NBER Methodology Revisited," CEPR Discussion Papers 2400, C.E.P.R. Discussion Papers.
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    5. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
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    7. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November.
    8. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc.
    9. Michael P. Clements & David F. Hendry, 2001. "Forecasting Non-Stationary Economic Time Series," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262531895.
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