Optimal bandwidth selection in heteroskedasticity-autocorrelation robust testing
成果类型：
Article
署名作者：
Sun, Yixiao; Phillips, Peter C. B.; Jin, Sainan
署名单位：
University of California System; University of California San Diego; Yale University; University of Auckland; University of York - UK; Peking University
刊物名称：
ECONOMETRICA
ISSN/ISSBN：
0012-9682
DOI：
10.1111/j.0012-9682.2008.00822.x
发表日期：
2008
页码：
175-194
关键词：
摘要：
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.
来源URL：