Optimal Bandwidth Selection in Heteroskedasticity-Autocorrelation Robust Testing
Yixiao Sun (),
Peter Phillips () and
Econometrica, 2008, vol. 76, issue 1, 175-194
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.
References: Add references at CitEc
Citations: View citations in EconPapers (89) Track citations by RSS feed
Downloads: (external link)
http://hdl.handle.net/10.1111/j.1468-0262.2008.00822.x link to full text (text/html)
Access to full text is restricted to subscribers.
Working Paper: Optimal Bandwidth Selection in Heteroskedasticity-Autocorrelation Robust Testing (2006)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ecm:emetrp:v:76:y:2008:i:1:p:175-194
Ordering information: This journal article can be ordered from
https://www.economet ... ordering-back-issues
Access Statistics for this article
Econometrica is currently edited by Guido Imbens
More articles in Econometrica from Econometric Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().