Optimal bandwidth selection in heteroskedasticity-autocorrelation robust testing
Optimal bandwidth selection in heteroskedasticity-autocorrelation robust testing
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.
asymptotic expansion, bandwidth choice, kernel method, long-run variance, loss function, nonstandard asymptotics, robust standard error, type I and type II errors
175-194
Sun, Yixiao
054e9dc7-1b11-4274-bc37-2920a484473b
Phillips, Peter C.B.
f67573a4-fc30-484c-ad74-4bbc797d7243
Jin, Sainan
faa63244-5d92-4f1b-9851-c113cc61c0ea
January 2008
Sun, Yixiao
054e9dc7-1b11-4274-bc37-2920a484473b
Phillips, Peter C.B.
f67573a4-fc30-484c-ad74-4bbc797d7243
Jin, Sainan
faa63244-5d92-4f1b-9851-c113cc61c0ea
Sun, Yixiao, Phillips, Peter C.B. and Jin, Sainan
(2008)
Optimal bandwidth selection in heteroskedasticity-autocorrelation robust testing.
Econometrica, 76 (1), .
(doi:10.1111/j.0012-9682.2008.00822.x).
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.
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Published date: January 2008
Keywords:
asymptotic expansion, bandwidth choice, kernel method, long-run variance, loss function, nonstandard asymptotics, robust standard error, type I and type II errors
Organisations:
Social Sciences
Identifiers
Local EPrints ID: 358333
URI: http://eprints.soton.ac.uk/id/eprint/358333
ISSN: 0012-9682
PURE UUID: e95002f5-9ec2-455f-bd23-fe51d8d25912
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Date deposited: 03 Oct 2013 13:38
Last modified: 14 Mar 2024 15:03
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Author:
Yixiao Sun
Author:
Sainan Jin
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