{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T04:48:05Z","timestamp":1771044485334,"version":"3.50.1"},"reference-count":23,"publisher":"Cambridge University Press (CUP)","issue":"1","license":[{"start":{"date-parts":[[2005,2,8]],"date-time":"2005-02-08T00:00:00Z","timestamp":1107820800000},"content-version":"unspecified","delay-in-days":7,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Econom. Theory"],"published-print":{"date-parts":[[2005,2]]},"abstract":"<jats:p>This paper proposes a new class of heteroskedastic and \nautocorrelation consistent (HAC) covariance matrix estimators. The \nstandard HAC estimation method reweights estimators of the \nautocovariances. Here we initially smooth the data observations \nthemselves using kernel function\u2013based weights. The resultant HAC \ncovariance matrix estimator is the normalized outer product of the \nsmoothed random vectors and is therefore automatically positive \nsemidefinite. A corresponding efficient GMM criterion may also be \ndefined as a quadratic form in the smoothed moment indicators whose \nnormalized minimand provides a test statistic for the overidentifying \nmoment conditions.<\/jats:p>","DOI":"10.1017\/s0266466605050103","type":"journal-article","created":{"date-parts":[[2005,4,25]],"date-time":"2005-04-25T14:15:35Z","timestamp":1114438535000},"page":"158-170","source":"Crossref","is-referenced-by-count":24,"title":["AUTOMATIC POSITIVE SEMIDEFINITE HAC COVARIANCE MATRIX AND GMM \nESTIMATION"],"prefix":"10.1017","volume":"21","author":[{"given":"Richard J.","family":"Smith","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2005,2,8]]},"reference":[{"key":"S0266466605050103_ref005","unstructured":"Grenander, U. & M. 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