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 is an element of (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. Copyright The Econometric Society 2008.
Year of publication: |
2008
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Authors: | Sun, Yixiao ; Phillips, Peter C. B. ; Jin, Sainan |
Published in: |
Econometrica. - Econometric Society. - Vol. 76.2008, 1, p. 175-194
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Publisher: |
Econometric Society |
Saved in:
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