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Forecast pooling for short time series of macroeconomic variables

  • Massimiliano Marcellino

It is rather common to have several competing forecasts for the same variable, and many methods have been suggested to pick up the best, on the basis of their past forecasting performance. As an alternative, the forecasts can be combined to obtain a pooled forecast, and several options are available to select what forecasts should be pooled, and how to determine their relative weights. In this paper we compare the relative performance of alternative pooling methods, using a very large dataset of about 500 macroeconomic variables for the countries in the European Monetary Union. In this case the forecasting exercise is further complicated by the short time span available, due to the need of collecting a homogeneous dataset. For each variable in the dataset, we consider 58 forecasts produced by a range of linear, time-varying and non-linear models, plus 16 pooled forecasts. Our results indicate that on average combination methods work well. Yet, a more disaggregate analysis reveals that single non-linear models can outperform combination forecasts for several series, even though they perform rather badly for other series so that on average their performance is not as good as that of pooled forecasts. Similar results are obtained for a subset of unstable series, the pooled forecasts behave only slightly better, and for three key macroeconomic variables, namely, industrial production, unemployment and inflation.

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Paper provided by IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University in its series Working Papers with number 212.

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Handle: RePEc:igi:igierp:212
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  1. 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.
  2. James H. Stock & Mark W. Watson, 1994. "Evidence on structural instability in macroeconomic times series relations," Working Paper Series, Macroeconomic Issues 94-13, Federal Reserve Bank of Chicago.
  3. Artis, M. & Marcellino, M., 1999. "Fiscal Forecasting: the Track Record of the IMF, OECD and EC," Economics Working Papers eco99/22, European University Institute.
  4. Francis X. Diebold & Lutz Kilian, 1999. "Unit Root Tests are Useful for Selecting Forecasting Models," New York University, Leonard N. Stern School Finance Department Working Paper Seires 99-063, New York University, Leonard N. Stern School of Business-.
  5. Andrews, Donald W K & Ploberger, Werner, 1994. "Optimal Tests When a Nuisance Parameter Is Present Only under the Alternative," Econometrica, Econometric Society, vol. 62(6), pages 1383-1414, November.
  6. Marcellino, Massimiliano, 2002. "Instability and Non-Linearity in the EMU," CEPR Discussion Papers 3312, C.E.P.R. Discussion Papers.
  7. Norman R. Swanson & Halbert White, 1995. "A Model Selection Approach to Real-Time Macroeconomic Forecasting Using Linear Models and Artificial Neural Networks," Macroeconomics 9503004, EconWPA.
  8. Stock, James H, 1996. "VAR, Error Correction and Pretest Forecasts at Long Horizons," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 58(4), pages 685-701, November.
  9. Clements, Michael P. & Hendry, David F., 1996. "Multi-Step Estimation for Forecasting," The Warwick Economics Research Paper Series (TWERPS) 447, University of Warwick, Department of Economics.
  10. Meese, Richard & Geweke, John, 1984. "A Comparison of Autoregressive Univariate Forecasting Procedures for Macroeconomic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(3), pages 191-200, July.
  11. David F. Hendry & Michael P. Clements, 2004. "Pooling of forecasts," Econometrics Journal, Royal Economic Society, vol. 7(1), pages 1-31, 06.
  12. James H. Stock & Mark W. Watson, 1998. "A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series," NBER Working Papers 6607, National Bureau of Economic Research, Inc.
  13. Graham Elliott & Thomas J. Rothenberg & James H. Stock, 1992. "Efficient Tests for an Autoregressive Unit Root," NBER Technical Working Papers 0130, National Bureau of Economic Research, Inc.
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