This file is part of IDEAS, which uses RePEc data


[ Papers | Articles | Software | Books | Chapters | Authors | Institutions | JEL Classification | NEP reports | Search | New papers by email | Author registration | Rankings | Volunteers | FAQ | Blog | Help! ]

Forecasting using a large number of predictors: is Bayesian regression a valid alternative to principal components?

Author info | Abstract | Publisher info | Download info | Related research | Statistics
Author Info
De Mol, Christine
Giannone, Domenico
Reichlin, Lucrezia

Additional information is available for the following registered author(s):

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.

Download Info
To download:

If you experience problems downloading a file, check if you have the proper application to view it first. Information about this may be contained in the File-Format links below. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.

File URL: http://hdl.handle.net/10419/19661
File Format: application/pdf
File Function:
Download Restriction: no

Publisher Info
Paper provided by Deutsche Bundesbank, Research Centre in its series Discussion Paper Series 1: Economic Studies with number 2006,32.

Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Length:
Date of creation: 2006
Date of revision:
Handle: RePEc:zbw:bubdp1:5040

Contact details of provider:
Postal: Postfach 10 06 02, 60006 Frankfurt
Phone: 0 69 / 95 66 - 34 55
Fax: 0 69 / 95 66 30 77
Email:
Web page: http://www.bundesbank.de/
More information through EDIRC

For technical questions regarding this item, or to correct its listing, contact: (ZBW - German National Library for Economics).

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

Other versions of this item:

Find related papers by JEL classification:
C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data
C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Estimation
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis

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!]
    Other versions:
  2. Chamberlain, Gary & Rothschild, Michael, 1983. "Arbitrage, Factor Structure, and Mean-Variance Analysis on Large Asset Markets," Econometrica, Econometric Society, vol. 51(5), pages 1281-304, September. [Downloadable!] (restricted)
    Other versions:
  3. 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!]
    Other versions:
  4. 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)
  5. 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)
    Other versions:
  6. Antonello D’Agostino & Domenico Giannone & Paolo Surico, 2006. "(Un)Predictability and macroeconomic stability," Working Paper Series 605, European Central Bank. [Downloadable!]
    Other versions:
  7. 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)
    Other versions:
  8. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November. [Downloadable!] (restricted)
    Other versions:
  9. 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)
    Other versions:
  10. 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.
    Other versions:
  11. Jushan Bai, 2003. "Inferential Theory for Factor Models of Large Dimensions," Econometrica, Econometric Society, vol. 71(1), pages 135-171, January. [Downloadable!] (restricted)
  12. 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)
  13. Antonello D'Agostino & Domenico Giannone, 2006. "Comparing alternative predictors based on large-panel factor models," Working Paper Series 680, European Central Bank. [Downloadable!]
    Other versions:
  14. 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)
    Other versions:
  15. 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!]
    Other versions:
  16. 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)
    Other versions:
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. Alexander Chudik & M. Hashem Pesaran, 2009. "Infinite-dimensional VARs and factor models," Working Paper Series 998, European Central Bank. [Downloadable!]
    Other versions:
  2. 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:
  3. 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!]
  4. Massimiliano Marcellino & Christian Schumacher, 2008. "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:
  5. Banbura, Marta & Giannone, Domenico & Reichlin, Lucrezia, 2007. "Bayesian VARs with Large Panels," CEPR Discussion Papers 6326, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
  6. Jushan Bai & Serena Ng, 2009. "Boosting diffusion indices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(4), pages 607-629. [Downloadable!]
  7. 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!]
  8. A. Carriero & G. Kapetanios & M. Marcellino, 2008. "Forecasting Exchange Rates with a Large Bayesian VAR," Economics Working Papers ECO2008/33, European University Institute. [Downloadable!]
    Other versions:
  9. 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:
  10. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2008. "Large Bayesian VARs," ECARES Working Papers 2008_033, Université Libre de Bruxelles, Ecares. [Downloadable!]
    Other versions:
  11. Chris Bloor & Troy Matheson, 2008. "Analysing shock transmission in a data-rich environment: A large BVAR for New Zealand," Reserve Bank of New Zealand Discussion Paper Series DP2008/09, Reserve Bank of New Zealand. [Downloadable!]
  12. Rangan Gupta & Alain Kabundi, 2008. "Forecasting Macroeconomic Variables in a Small Open Economy: A Comparison between Small- and Large-Scale Models," Working Papers 200830, University of Pretoria, Department of Economics.
Statistics
Access and download statistics

Did you know? You too can volunteer for RePEc, for example by encouraging others to register as authors.

This page was last updated on 2009-11-22.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.