{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T20:03:35Z","timestamp":1793736215399,"version":"4.6.1"},"reference-count":32,"publisher":"Cambridge University Press (CUP)","issue":"1","license":[{"start":{"date-parts":[[2009,2,11]],"date-time":"2009-02-11T00:00:00Z","timestamp":1234310400000},"content-version":"unspecified","delay-in-days":6557,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Econom. Theory"],"published-print":{"date-parts":[[1991,3]]},"abstract":"<jats:p>An asymptotic optimality theory for the estimation of cointegration regressions is developed in this paper. The theory applies to a reasonably wide class of estimators without making any specific assumptions about the probability distribution or short-run dynamics of the data-generating process. Due to the nonstandard nature of the estimation problem, the conventional minimum variance criterion does not provide a convenient measure of asymptotic efficiency. An alternative criterion, based on the concentration or peakedness of the limiting distribution of an estimator, is therefore adopted. The limiting distribution of estimators with maximum asymptotic efficiency is characterized in the paper and used to discuss the optimality of some known estimators. A new asymptotically efficient estimator is also introduced. This estimator is obtained from the ordinary least-squares estimator by a time domain correction which is nonparametric in the sense that no assumption of a finite parameter model is required. The estimator can be computed with least squares without any initial estimations.<\/jats:p>","DOI":"10.1017\/s0266466600004217","type":"journal-article","created":{"date-parts":[[2009,3,10]],"date-time":"2009-03-10T15:46:16Z","timestamp":1236699976000},"page":"1-21","source":"Crossref","is-referenced-by-count":955,"title":["Asymptotically Efficient Estimation of Cointegration Regressions"],"prefix":"10.1017","volume":"7","author":[{"given":"Pentti","family":"Saikkonen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2009,2,11]]},"reference":[{"key":"S0266466600004217_ref014","doi-asserted-by":"publisher","DOI":"10.1016\/0165-1889(88)90041-3"},{"key":"S0266466600004217_ref002","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-5505-5"},{"key":"S0266466600004217_ref031","doi-asserted-by":"publisher","DOI":"10.2307\/1911260"},{"key":"S0266466600004217_ref029","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/71.3.599"},{"key":"S0266466600004217_ref027","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1988.10478573"},{"key":"S0266466600004217_ref026","unstructured":"26. 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