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Improved nonparametric confidence intervals in time series regressions

Joseph P. Romano and Michael Wolf ()

Economics Working Papers from Department of Economics and Business, Universitat Pompeu Fabra

Abstract: Con dence intervals in econometric time series regressions suffer from notorious coverage problems. This is especially true when the dependence in the data is noticeable and sample sizes are small to moderate, as is often the case in empirical studies. This paper suggests using the studentized block bootstrap and discusses practical issues, such as the choice of the block size. A particular data-dependent method is proposed to automate the method. As a side note, it is pointed out that symmetric confidence intervals are preferred over equal-tailed ones, since they exhibit improved coverage accuracy. The improvements in small sample performance are supported by a simulation study.

Keywords: Bootstrap; confidence intervals; studentization; time series regressions; prewhitening (search for similar items in EconPapers)
JEL-codes: C14 C15 C22 C32 (search for similar items in EconPapers)
Date: 2002-07
New Economics Papers: this item is included in nep-ecm and nep-ets
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Related works:
Working Paper: Improved Nonparametric Confidence Intervals in Time Series Regressions (2006) Downloads
Working Paper: Improved nonparametric confidence intervals in time series regressions (2001) Downloads
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