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Improved nonparametric confidence intervals in time series regressions


Romano, Joseph P; Wolf, Michael (2006). Improved nonparametric confidence intervals in time series regressions. Journal of Nonparametric Statistics, 18(2):199-214.

Abstract

Confidence 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 article 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, as they exhibit improved coverage accuracy. The improvements in small sample performance are supported by a simulation study.

Abstract

Confidence 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 article 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, as they exhibit improved coverage accuracy. The improvements in small sample performance are supported by a simulation study.

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Additional indexing

Item Type:Journal Article, refereed
Communities & Collections:03 Faculty of Economics > Department of Economics
Dewey Decimal Classification:330 Economics
Uncontrolled Keywords:Bootstrap, Confidence intervals, Studentization, Time series regressions, Prewithening
Language:English
Date:February 2006
Deposited On:11 Feb 2008 12:29
Last Modified:19 Feb 2018 06:43
Publisher:Taylor & Francis
ISSN:1026-7654
OA Status:Closed
Publisher DOI:https://doi.org/10.1080/10485250600687812

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