{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,20]],"date-time":"2026-05-20T14:16:05Z","timestamp":1779286565923,"version":"3.51.4"},"reference-count":0,"publisher":"Cambridge University Press (CUP)","issue":"3","license":[{"start":{"date-parts":[[2002,5,15]],"date-time":"2002-05-15T00:00:00Z","timestamp":1021420800000},"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":[[2002,6]]},"abstract":"<jats:p>This paper analyzes autoregressive time series models where \nthe errors are assumed to be martingale difference sequences \nthat satisfy an additional symmetry condition on their fourth-order \nmoments. Under these conditions quasi maximum likelihood estimators \nof the autoregressive parameters are no longer efficient in \nthe generalized method of moments (GMM) sense. The main result \nof the paper is the construction of efficient semiparametric \ninstrumental variables estimators for the autoregressive \nparameters. The optimal instruments are linear functions of \nthe innovation sequence.<\/jats:p><jats:p>It is shown that a frequency domain approximation of the optimal \ninstruments leads to an estimator that only depends on the data \nperiodogram and an unknown linear filter. Semiparametric methods \nto estimate the optimal filter are proposed.<\/jats:p><jats:p>The procedure is equivalent to GMM estimators where lagged \nobservations are used as instruments. As a result of the additional \nsymmetry assumption on the fourth moments the number of instruments \nis allowed to grow at the same rate as the sample. No lag \ntruncation parameters are needed to implement the estimator, \nwhich makes it particularly appealing from an applied point \nof view.<\/jats:p>","DOI":"10.1017\/s0266466602183010","type":"journal-article","created":{"date-parts":[[2002,12,11]],"date-time":"2002-12-11T22:13:26Z","timestamp":1039644806000},"page":"547-583","source":"Crossref","is-referenced-by-count":23,"title":["EFFICIENT IV ESTIMATION FOR AUTOREGRESSIVE MODELS WITH \nCONDITIONAL HETEROSKEDASTICITY"],"prefix":"10.1017","volume":"18","author":[{"given":"Guido M.","family":"Kuersteiner","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2002,5,15]]},"container-title":["Econometric Theory"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0266466602183010","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,4,4]],"date-time":"2019-04-04T19:50:54Z","timestamp":1554407454000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0266466602183010\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2002,5,15]]},"references-count":0,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2002,6]]}},"alternative-id":["S0266466602183010"],"URL":"https:\/\/doi.org\/10.1017\/s0266466602183010","relation":{},"ISSN":["0266-4666","1469-4360"],"issn-type":[{"value":"0266-4666","type":"print"},{"value":"1469-4360","type":"electronic"}],"subject":[],"published":{"date-parts":[[2002,5,15]]}}}