Optimal Bandwidth Selection in Heteroskedasticity-Autocorrelation Robust Testing.”Forthcoming, Econometrica

04-142254. Jin acknowledges \u85nancial support from the NSFC (Grant No. 70601001). In time series regressions with nonparametrically autocorrelated errors, it is now stan-dard empirical practice to use kernel-based robust standard errors that involve some smoothing function over the sample autocovariances. The underlying smoothing parame-ter b, which can be de\u85ned as the ratio of the bandwidth (or truncation lag) to the sample size, is a tuning parameter that plays a key role in determining the asymptotic properties of the standard errors and associated semiparametric tests. Small-b asymptotics involve standard limit theory such as standard normal or chi-squared limits, whereas \u85xed-b as-ymptotics typically lead to nonstandard limit distributions involving Brownian bridge functionals. The present paper shows that the nonstandard \u85xed-b limit distributions of such nonparametrically studentized tests provide more accurate approximations to the nite sample distributions than the standard small-b limit distribution. In particular, using asymptotic expansions of both the \u85nite sample distribution and the nonstandard limit distribution, we con\u85rm that the second-order corrected critical value based on th