Advanced Search
MyIDEAS: Login to save this paper or follow this series

Bagging Time Series Models

Contents:

Author Info

  • Lutz Kilian
  • Atsushi Inoue

Abstract

A common problem in out-of-sample prediction is that there are potentially many relevant predictors that individually have only weak explanatory power. We propose bootstrap aggregation of pre-test predictors (or bagging for short) as a means of constructing forecasts from multiple regression models with local-to-zero regression parameters and errors subject to possible serial correlation or conditional heteroskedasticity. Bagging is designed for situations in which the number of predictors (M) is moderately large relative to the sample size (T). We show how to implement bagging in the dynamic multiple regression model and provide asymptotic justification for the bagging predictor. A simulation study shows that bagging tends to produce large reductions in the out-of-sample prediction mean squared error and provides a useful alternative to forecasting from factor models when M is large, but much smaller than T. We also find that bagging indicators of real economic activity greatly redcues the prediction mean squared error of forecasts of U.S. CPI inflation at horizons of one month and one year

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. 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://repec.org/esNASM04/up.22748.1074182942.pdf
Download Restriction: no

Bibliographic Info

Paper provided by Econometric Society in its series Econometric Society 2004 North American Summer Meetings with number 110.

as in new window
Length:
Date of creation: 11 Aug 2004
Date of revision:
Handle: RePEc:ecm:nasm04:110

Contact details of provider:
Phone: 1 212 998 3820
Fax: 1 212 995 4487
Email:
Web page: http://www.econometricsociety.org/pastmeetings.asp
More information through EDIRC

Related research

Keywords: forecasting; bootstrap; model selection; pre-testing; forecast aggregation; factor models; inflation.;

Other versions of this item:

Find related papers by JEL classification:

This paper has been announced in the following NEP Reports:

References

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.:
as in new window
  1. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2003. "The Generalized Dynamic Factor Model. One-Sided Estimation and Forecasting," LEM Papers Series 2003/13, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
  2. Inoue, Atsushi & Kilian, Lutz, 2003. "On the Selection of Forecasting Models," CEPR Discussion Papers 3809, C.E.P.R. Discussion Papers.
  3. Forni, Mario & Hallin, Marc & Lippi, Marco & Reichlin, Lucrezia, 2003. "Do financial variables help forecasting inflation and real activity in the euro area?," Journal of Monetary Economics, Elsevier, vol. 50(6), pages 1243-1255, September.
  4. Massimiliano Marcellino & James H. Stock & Mark W. Watson, . "Macroeconomic Forecasting in the Euro Area: Country Specific versus Area-Wide Information," Working Papers 201, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
  5. Inoue, Atsushi & Shintani, Mototsugu, 2006. "Bootstrapping GMM estimators for time series," Journal of Econometrics, Elsevier, vol. 133(2), pages 531-555, August.
  6. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  7. Mario Forni & Marc Hallin & Lucrezia Reichlin & Marco Lippi, 2000. "The generalised dynamic factor model: identification and estimation," ULB Institutional Repository 2013/10143, ULB -- Universite Libre de Bruxelles.
  8. Sílvia Gonçalves & Halbert White, 2002. "Maximum Likelihood and the Bootstrap for Nonlinear Dynamic Models," CIRANO Working Papers 2002s-41, CIRANO.
  9. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-38, May.
  10. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
  11. Hall, Peter & Horowitz, Joel L, 1996. "Bootstrap Critical Values for Tests Based on Generalized-Method-of-Moments Estimators," Econometrica, Econometric Society, vol. 64(4), pages 891-916, July.
  12. Bernanke, Ben S. & Boivin, Jean, 2003. "Monetary policy in a data-rich environment," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 525-546, April.
  13. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
  14. James H. Stock & Mark W. Watson, 2001. "Forecasting Output and Inflation: The Role of Asset Prices," NBER Working Papers 8180, National Bureau of Economic Research, Inc.
  15. Kilian, Lutz & Gonçalves, Sílvia, 2002. "Bootstrapping Autoregressions with Conditional Heteroskedasticity of Unknown Form," Discussion Paper Series 1: Economic Studies 2002,26, Deutsche Bundesbank, Research Centre.
  16. Kenneth D. West, 1995. "Another Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator," NBER Technical Working Papers 0183, National Bureau of Economic Research, Inc.
  17. Donald W.K. Andrews, 1999. "Higher-Order Improvements of a Computationally Attractive-Step Bootstrap for Extremum Estimators," Cowles Foundation Discussion Papers 1230, Cowles Foundation for Research in Economics, Yale University.
  18. Thomson, Michael & Schmidt, Peter, 1982. "A Note on the Comparison of the Mean Square Error of Inequality Constrained Least Squares and Other Related Estimators," The Review of Economics and Statistics, MIT Press, vol. 64(1), pages 174-76, February.
  19. Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc.
  20. 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.
Full references (including those not matched with items on IDEAS)

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as in new window

Cited by:
  1. Carriero, Andrea & Kapetanios, George & Marcellino, Massimiliano, 2009. "Forecasting Large Datasets with Bayesian Reduced Rank Multivariate Models," CEPR Discussion Papers 7446, C.E.P.R. Discussion Papers.
  2. Andrea Carriero & George Kapetanios & Massimiliano Marcellino, 2007. "Forecasting Large Datasets with Reduced Rank Multivariate Models," Working Papers 617, Queen Mary, University of London, School of Economics and Finance.
  3. Ron Alquist & Lutz Kilian & Robert J. Vigfusson, 2011. "Forecasting the Price of Oil," Working Papers 11-15, Bank of Canada.
  4. Eric Hillebrand & Marcelo Cunha Medeiros, 2007. "Forecasting realized volatility models:the benefits of bagging and nonlinear specifications," Textos para discussão 547, Department of Economics PUC-Rio (Brazil).
  5. Michael McAleer & Marcelo C. Medeiros, 2009. "Forecasting Realized Volatility with Linear and Nonlinear Models," CIRJE F-Series CIRJE-F-686, CIRJE, Faculty of Economics, University of Tokyo.
  6. Todd E. Clark & Michael W. McCracken, 2009. "In-sample tests of predictive ability: a new approach," Working Papers 2009-051, Federal Reserve Bank of St. Louis.

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:ecm:nasm04:110. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum).

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.