Recent developments in bootstrapping time series

Jeremy Berkowitz and Lutz Kilian

No 96-45, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

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.

Keywords: time; series; analysis (search for similar items in EconPapers)
Date: 1996
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Citations: View citations in EconPapers (50)

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Related works:
Working Paper: Recent Developments in Bootstrapping Time Series (2019)
Journal Article: Recent developments in bootstrapping time series (2000)
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