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

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

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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Article provided by Elsevier in its journal European Economic Review.

Volume (Year): 47 (2003)
Issue (Month): 1 (February)
Pages: 1-18

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Handle: RePEc:eee:eecrev:v:47:y:2003:i:1:p:1-18
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  1. Gali, Jordi & Gertler, Mark & Lopez-Salido, J. David, 2001. "European inflation dynamics," European Economic Review, Elsevier, vol. 45(7), pages 1237-1270.
  2. Mario Forni & Marc Hallin & Lucrezia Reichlin & Marco Lippi, 2000. "The generalised dynamic factor model: identification and estimation," ULB Institutional Repository 2013/10143, ULB -- Universite Libre de Bruxelles.
  3. James H. Stock & Mark W. Watson, 1998. "Diffusion Indexes," NBER Working Papers 6702, National Bureau of Economic Research, Inc.
  4. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
  5. Fair, Ray C & Shiller, Robert J, 1990. "Comparing Information in Forecasts from Econometric Models," American Economic Review, American Economic Association, vol. 80(3), pages 375-89, June.
  6. Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Econometric Society World Congress 2000 Contributed Papers 1504, Econometric Society.
  7. Thomas J. Sargent & Christopher A. Sims, 1977. "Business cycle modeling without pretending to have too much a priori economic theory," Working Papers 55, Federal Reserve Bank of Minneapolis.
  8. 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.
  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, June.
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