{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,28]],"date-time":"2026-07-28T05:03:34Z","timestamp":1785215014448,"version":"3.55.0"},"reference-count":0,"publisher":"Cambridge University Press (CUP)","issue":"6","license":[{"start":{"date-parts":[[2002,9,24]],"date-time":"2002-09-24T00:00:00Z","timestamp":1032825600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Econom. Theory"],"published-print":{"date-parts":[[2002,12]]},"abstract":"<jats:p>Asymptotic theory for heteroskedasticity autocorrelation \nconsistent (HAC) covariance matrix estimators requires the \ntruncation lag, or bandwidth, to increase more slowly than the \nsample size. This paper considers an alternative approach covering \nthe case with the asymptotic covariance matrix estimated by \nkernel methods with truncation lag equal to sample size. Although \nsuch estimators are inconsistent, valid tests (asymptotically \npivotal) for regression parameters can be constructed. The limiting \ndistributions explicitly capture the truncation lag and choice \nof kernel. A local asymptotic power analysis shows that the \nBartlett kernel delivers the highest power within a group of \npopular kernels. Finite sample simulations suggest that, regardless \nof the kernel chosen, the null asymptotic approximation of the \nnew tests is often more accurate than that for conventional \nHAC estimators and asymptotics. Finite sample results on power \nshow that the new approach is competitive.<\/jats:p>","DOI":"10.1017\/s026646660218604x","type":"journal-article","created":{"date-parts":[[2003,10,16]],"date-time":"2003-10-16T10:06:57Z","timestamp":1066298817000},"page":"1350-1366","source":"Crossref","is-referenced-by-count":122,"title":["HETEROSKEDASTICITY-AUTOCORRELATION ROBUST TESTING USING \nBANDWIDTH EQUAL TO SAMPLE SIZE"],"prefix":"10.1017","volume":"18","author":[{"given":"Nicholas M.","family":"Kiefer","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy J.","family":"Vogelsang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2002,9,24]]},"container-title":["Econometric Theory"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S026646660218604X","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,7]],"date-time":"2019-05-07T00:18:49Z","timestamp":1557188329000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S026646660218604X\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2002,9,24]]},"references-count":0,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2002,12]]}},"alternative-id":["S026646660218604X"],"URL":"https:\/\/doi.org\/10.1017\/s026646660218604x","relation":{},"ISSN":["0266-4666","1469-4360"],"issn-type":[{"value":"0266-4666","type":"print"},{"value":"1469-4360","type":"electronic"}],"subject":[],"published":{"date-parts":[[2002,9,24]]}}}