{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T07:39:11Z","timestamp":1774769951726,"version":"3.50.1"},"reference-count":0,"publisher":"Cambridge University Press (CUP)","issue":"5","license":[{"start":{"date-parts":[[1998,10,1]],"date-time":"1998-10-01T00:00:00Z","timestamp":907200000000},"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":[[1998,10]]},"abstract":"<jats:p>Many unit root and cointegration tests require \nan estimate of the spectral density function at frequency \nzero of some process. Commonly used are kernel estimators \nbased on weighted sums of autocovariances constructed using \nestimated residuals from an AR(1) regression. However, \nit is known that with substantially correlated errors, \nthe OLS estimate of the AR(1) parameter is severely biased. \nIn this paper, we first show that this least-squares bias \ninduces a significant increase in the bias and mean-squared \nerror (MSE) of kernel-based estimators. We then consider \na variant of the autoregressive spectral density estimator \nthat does not share these shortcomings because it bypasses \nthe use of the estimate from the AR(1) regression. Simulations \nand local asymptotic analyses show its bias and MSE to \nbe much smaller than those of a kernel-based estimator \nwhen there is strong negative serial correlation. We also \ninclude a discussion about the appropriate choice of the \ntruncation lag.<\/jats:p>","DOI":"10.1017\/s0266466698145024","type":"journal-article","created":{"date-parts":[[2002,7,27]],"date-time":"2002-07-27T09:45:46Z","timestamp":1027763146000},"page":"560-603","source":"Crossref","is-referenced-by-count":40,"title":["AN AUTOREGRESSIVE SPECTRAL DENSITY ESTIMATOR AT FREQUENCY \nZERO FOR NONSTATIONARITY TESTS"],"prefix":"10.1017","volume":"14","author":[{"given":"Pierre","family":"Perron","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Serena","family":"Ng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[1998,10,1]]},"container-title":["Econometric Theory"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0266466698145024","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,10]],"date-time":"2019-05-10T16:02:57Z","timestamp":1557504177000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0266466698145024\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1998,10]]},"references-count":0,"journal-issue":{"issue":"5","published-print":{"date-parts":[[1998,10]]}},"alternative-id":["S0266466698145024"],"URL":"https:\/\/doi.org\/10.1017\/s0266466698145024","relation":{},"ISSN":["0266-4666","1469-4360"],"issn-type":[{"value":"0266-4666","type":"print"},{"value":"1469-4360","type":"electronic"}],"subject":[],"published":{"date-parts":[[1998,10]]}}}