Publication Type

Journal Article

Version

acceptedVersion

Publication Date

1-2008

Abstract

This paper considers studentized tests in time series regressions with nonparametrically autocorrelated errors. The studentization is based on robust standard errors with truncation lag M = bT for some constant b ∈ (0, 1] and sample size T. It is shown that the nonstandard fixed-b limit distributions of such nonparametrically studentized tests provide more accurate approximations to the finite sample distributions than the standard small-b limit distribution. We further show that, for typical economic time series, the optimal bandwidth that minimizes a weighted average of type I and type II errors is larger by an order of magnitude than the bandwidth that minimizes the asymptotic mean squared error of the corresponding long-run variance estimator. A plug-in procedure for implementing this optimal bandwidth is suggested and simulations (not reported here) confirm that the new plug-in procedure works well in finite samples.

Keywords

Asymptotic expansion, bandwidth choice, kernel method, long-run variance, loss function, nonstandard asymptotics, robust standard error, Type I and Type II errors

Discipline

Econometrics

Research Areas

Econometrics

Publication

Econometrica

Volume

76

Issue

1

First Page

175

Last Page

194

ISSN

0012-9682

Identifier

10.1111/j.0012-9682.2008.00822.x

Publisher

Econometric Society

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1111/j.0012-9682.2008.00822.x

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