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Variable selection, estimation and inference for multi-period forecasting problems

  • Pesaran, M. Hashem
  • Pick, Andreas
  • Timmermann, Allan

This paper conducts a broad-based comparison of iterated and direct multi-period forecasting approaches applied to both univariate and multivariate models in the form of parsimonious factor-augmented vector autoregressions. To account for serial correlation in the residuals of the multi-period direct forecasting models we propose a new SURE-based estimation method and modified Akaike information criteria for model selection. Empirical analysis of the 170 variables studied by Marcellino, Stock and Watson (2006) shows that information in factors helps improve forecasting performance for most types of economic variables although it can also lead to larger biases. It also shows that SURE estimation and finite-sample modifications to the Akaike information criterion can improve the performance of the direct multi-period forecasts.

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Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 164 (2011)
Issue (Month): 1 (September)
Pages: 173-187

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Handle: RePEc:eee:econom:v:164:y:2011:i:1:p:173-187
Contact details of provider: Web page: http://www.elsevier.com/locate/jeconom

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  2. Brown, Bryan W & Mariano, Roberto S, 1989. "Measures of Deterministic Prediction Bias in Nonlinear Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 30(3), pages 667-84, August.
  3. Carlo A. Favero & Andrea Tamoni, 2010. "Demographics and the Econometrics of the Term Structure of Stock Market Risk," Working Papers 367, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  4. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2004. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," NBER Working Papers 10220, National Bureau of Economic Research, Inc.
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  6. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
  7. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2005. "The Generalized Dynamic Factor Model: One-Sided Estimation and Forecasting," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 830-840, September.
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  9. Newey, Whitney K & West, Kenneth D, 1987. "A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix," Econometrica, Econometric Society, vol. 55(3), pages 703-08, May.
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  11. Schorfheide, Frank, 2005. "VAR forecasting under misspecification," Journal of Econometrics, Elsevier, vol. 128(1), pages 99-136, September.
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  14. Allan Timmermann & M. Hashem Pesaran, 2003. "Small Sample Properties of Forecasts from Autoregressive Models under Structural Breaks," CESifo Working Paper Series 990, CESifo Group Munich.
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  19. James H. Stock & Mark W. Watson, 2005. "Implications of Dynamic Factor Models for VAR Analysis," NBER Working Papers 11467, National Bureau of Economic Research, Inc.
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