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Forecasting using a large number of predictors - Is Bayesian regression a valid alternative to principal components?

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Author Info
Christine De Mol () (Universite Libre de Bruxelles – ECARES, Av. F. D. Roosevelt, 50 – CP 114, 1050 Bruxelles, Belgium.)
Domenico Giannone () (Universite Libre de Bruxelles – ECARES, Av. F. D. Roosevelt, 50 – CP 114, 1050 Bruxelles, Belgium.)
Lucrezia Reichlin () (European Central Bank, Kaiserstrasse 29, 60311 Frankfurt am Main, Germany.)

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Abstract

This paper considers Bayesian regression with normal and doubleexponential priors as forecasting methods based on large panels of time series. We show that, empirically, these forecasts are highly correlated with principal component forecasts and that they perform equally well for a wide range of prior choices. Moreover, we study the asymptotic properties of the Bayesian regression under Gaussian prior under the assumption that data are quasi collinear to establish a criterion for setting parameters in a large cross-section. JEL Classification: C11,C13, C33, C53.

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Paper provided by European Central Bank in its series Working Paper Series with number 700.

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Length: 33 pages
Date of creation: Dec 2006
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Handle: RePEc:ecb:ecbwps:20060700

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Related research
Keywords: Bayesian VAR ridge regression Lasso regression principal components large cross-sections.

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This paper has been announced in the following NEP Reports: References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Gary Koop & Simon Potter, 2003. "Forecasting in Large Macroeconomic Panels using Bayesian Model Averaging," Discussion Papers in Economics 04/16, Department of Economics, University of Leicester. [Downloadable!]
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  2. Stock J.H. & Watson M.W., 2002. "Forecasting Using Principal Components From a Large Number of Predictors," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 1167-1179, December. [Downloadable!] (restricted)
  3. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February. [Downloadable!] (restricted)
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  4. Antonello D’Agostino & Domenico Giannone & Paolo Surico, 2006. "(Un)Predictability and macroeconomic stability," Working Paper Series 605, European Central Bank. [Downloadable!]
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  5. Thomas Doan & Robert Litterman & Christopher Sims, 1984. "Forecasting and conditional projection using realistic prior distributions," Econometric Reviews, Taylor and Francis Journals, vol. 3(1), pages 1-100. [Downloadable!] (restricted)
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  6. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November. [Downloadable!] (restricted)
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  7. Forni, Mario & Lippi, Marco, 2001. "The Generalized Dynamic Factor Model: Representation Theory," Econometric Theory, Cambridge University Press, vol. 17(06), pages 1113-1141, December. [Downloadable!]
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  8. 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. [Downloadable!] (restricted)
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  9. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
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  10. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January. [Downloadable!] (restricted)
  11. 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. [Downloadable!] (restricted)
  12. Antonello D'Agostino & Domenico Giannone, 2006. "Comparing alternative predictors based on large-panel factor models," Working Paper Series 680, European Central Bank. [Downloadable!]
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  13. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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  14. Mario Forni & Domenico Giannone & Marco Lippi & Lucrezia Reichlin, 2007. "Opening the black box - structural factor models with large gross-sections," Working Paper Series 712, European Central Bank. [Downloadable!]
  15. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January. [Downloadable!] (restricted)
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Full references

Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Massimiliano Marcellino & Christian Schumacher, . "Factor-MIDAS for Now- and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP1," Working Papers 333, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University. [Downloadable!]
    Other versions:
  2. Rangan Gupta & Alain Kabundi, 2008. "Forecasting Macroeconomic Variables Using Large Datasets: Dynamic Factor Model versus Large-Scale BVARs," Working Papers 200816, University of Pretoria, Department of Economics. [Downloadable!]
  3. Alexander Chudik & M. Hashem Pesaran, 2007. "Infinite Dimensional VARs and Factor Models," IZA Discussion Papers 3206, Institute for the Study of Labor (IZA). [Downloadable!]
    Other versions:
  4. Domenico Giannone & Michele Lenza & Lucrezia Reichlin, 2008. "Explaining the Great Moderation - it is not the shocks," Working Paper Series 865, European Central Bank. [Downloadable!]
    Other versions:
  5. Scharnagl, Michael & Schumacher, Christian, 2007. "Reconsidering the role of monetary indicators for euro area inflation from a Bayesian perspective using group inclusion probabilities," Discussion Paper Series 1: Economic Studies 2007,09, Deutsche Bundesbank, Research Centre. [Downloadable!]
  6. Jan J. J. Groen & George Kapetanios, 2008. "Revisiting useful approaches to data-rich macroeconomic forecasting," Staff Reports 327, Federal Reserve Bank of New York. [Downloadable!]
    Other versions:
  7. Banbura, Marta & Giannone, Domenico & Reichlin, Lucrezia, 2007. "Bayesian VARs with Large Panels," CEPR Discussion Papers 6326, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
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