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Forecasting using a large number of predictors: Is Bayesian shrinkage a valid alternative to principal components?

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Author Info

  • De Mol, Christine
  • Giannone, Domenico
  • Reichlin, Lucrezia

Abstract

This paper considers Bayesian regression with normal and double-exponential 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 conditions for consistency of the forecast based on Bayesian regression as the cross-section and the sample size become large. This analysis serves as a guide to establish a criterion for setting the amount of shrinkage in a large cross-section.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 146 (2008)
Issue (Month): 2 (October)
Pages: 318-328
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Handle: RePEc:eee:econom:v:146:y:2008:i:2:p:318-328

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Web page: http://www.elsevier.com/locate/jeconom

For corrections or technical questions regarding this item, or to correct its listing, contact: (Jeroen Loos).

Related research

Keywords: Bayesian shrinkage Bayesian VAR Ridge regression Lasso regression Principal components Large cross-sections;

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Citations

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Cited by:
  1. KOROBILIS, Dimitris, 2011. "Hierarchical shrinkage priors for dynamic regressions with many predictors," CORE Discussion Papers 2011021, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. Kuzin, Vladimir N. & Marcellino, Massimiliano & Schumacher, Christian, 2009. "Pooling versus model selection for nowcasting with many predictors: an application to German GDP," Discussion Paper Series 1: Economic Studies 2009,03, Deutsche Bundesbank, Research Centre.
  3. BELMONTE, Miguel A.G. & KOOP, Gary & KOROBILIS, Dimitris, 2011. "Hierarchical shrinkage in time-varying parameter models," CORE Discussion Papers 2011036, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  4. Carriero, A. & Kapetanios, G. & Marcellino, M., 2009. "Forecasting exchange rates with a large Bayesian VAR," International Journal of Forecasting, Elsevier, vol. 25(2), pages 400-417.
  5. Rachida Ouysse, 2011. "Comparison of Bayesian moving Average and Principal Component Forecast for Large Dimensional Factor Models," Discussion Papers 2012-03, School of Economics, The University of New South Wales.
  6. Marek Jarociński, 2010. "Imposing parsimony in cross-country growth regressions," Working Paper Series 1234, European Central Bank.
  7. Kerstin Bernoth & Andreas Pick, 2009. "Forecasting the fragility of the banking and insurance sector," DNB Working Papers 202, Netherlands Central Bank, Research Department.
  8. Peter Exterkate & Patrick J.F. Groenen & Christiaan Heij & Dick van Dijk, 2011. "Nonlinear Forecasting with Many Predictors using Kernel Ridge Regression," Tinbergen Institute Discussion Papers 11-007/4, Tinbergen Institute.
  9. Ching Wai (Jeremy) Chiu & Bjørn Eraker & Andrew T. Foerster & Tae Bong Kim & Hernán D. Seoane, 2011. "Estimating VAR's sampled at mixed or irregular spaced frequencies : a Bayesian approach," Research Working Paper RWP 11-11, Federal Reserve Bank of Kansas City.

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