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 distributi...
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| المؤلفون الرئيسيون: | , , |
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| التنسيق: | text |
| اللغة: | English |
| منشور في: |
Institutional Knowledge at Singapore Management University
2008
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://ink.library.smu.edu.sg/soe_research/61 https://ink.library.smu.edu.sg/context/soe_research/article/1060/viewcontent/HAR_testing_av.pdf |
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| المؤسسة: | Singapore Management University |
| اللغة: | English |
| الملخص: | 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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