Paper #635

Title:

Improved nonparametric confidence intervals in time series regressions

Authors:

Joseph P. Romano and Michael Wolf

Date:

July 2002

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

JEL codes:

C14, C15, C22, C32

Area of Research:

Statistics, Econometrics and Quantitative Methods

Published in:

Journal of Nonparametric Statistics, Volume 18, Number 2, February 2006, pp. 199-214

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