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 âˆˆ (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
PHILLIPS, Peter C. B.; JIN, Sainan; and SUN, Yixiao.
Optimal Bandwidth Selection in Heteroskedasticity-Autocorrelation Robust Testing. (2008). Econometrica. 76, (1), 175-194. Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/61