Recent Developments in Bootstrapping Time Series

Posted: 20 Dec 1996

See all articles by Jeremy Berkowitz

Jeremy Berkowitz

University of Houston - Department of Finance

Lutz Kilian

Federal Reserve Banks - Federal Reserve Bank of Dallas; Centre for Economic Policy Research (CEPR)

Date Written: November 8, 1996

Abstract

In recent years, several new parametric and nonparametric bootstrap methods have been proposed for time series data. Which of these methods should applied researchers use? We provide evidence that for many applications in time series econometrics parametric methods are more accurate, and we identify directions for future research on improving nonparametric methods. We explicitly address the important but often neglected issue of model selection in bootstrapping. In particular, we emphasize the advantages of the AIC over other lag order selection criteria and the need to account for lag order uncertainty in resampling. We also show that the block size plays an important role in determining the success of the block bootstrap, and we propose a data-based block size selection procedure.

JEL Classification: C13, C22

Suggested Citation

Berkowitz, Jeremy and Kilian, Lutz, Recent Developments in Bootstrapping Time Series (November 8, 1996). Available at SSRN: https://ssrn.com/abstract=3810

Jeremy Berkowitz (Contact Author)

University of Houston - Department of Finance ( email )

Houston, TX 77204
United States

Lutz Kilian

Federal Reserve Banks - Federal Reserve Bank of Dallas ( email )

2200 North Pearl Street
PO Box 655906
Dallas, TX 75265-5906
United States

Centre for Economic Policy Research (CEPR)

London
United Kingdom

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