{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T20:03:47Z","timestamp":1792008227864,"version":"4.4.0"},"reference-count":23,"publisher":"Cambridge University Press (CUP)","issue":"1","license":[{"start":{"date-parts":[[2009,2,1]],"date-time":"2009-02-01T00:00:00Z","timestamp":1233446400000},"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":[[2009,2]]},"abstract":"<jats:p>This paper proposes the least absolute shrinkage and selection operator\u2013type (Lasso-type) generalized method of moments (GMM) estimator. This Lasso-type estimator is formed by the GMM objective function with the addition of a penalty term. The exponent of the penalty term in the regular Lasso estimator is equal to one. However, the exponent of the penalty term in the Lasso-type estimator is less than one in the analysis here. The magnitude of the exponent is reduced to avoid the asymptotic bias. This estimator selects the correct model and estimates it simultaneously. In other words, this method estimates the redundant parameters as zero in the large samples and provides the standard GMM limit distribution for the estimates of the nonzero parameters in the model. The asymptotic theory for our estimator is nonstandard. We conduct a simulation study that shows that the Lasso-type GMM correctly selects the true model much more often than the Bayesian information Criterion (BIC) and another model selection procedure based on the GMM objective function.<\/jats:p>","DOI":"10.1017\/s0266466608090099","type":"journal-article","created":{"date-parts":[[2009,1,9]],"date-time":"2009-01-09T14:13:57Z","timestamp":1231510437000},"page":"270-290","source":"Crossref","is-referenced-by-count":113,"title":["LASSO-TYPE GMM ESTIMATOR"],"prefix":"10.1017","volume":"25","author":[{"given":"Mehmet","family":"Caner","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2009,2,1]]},"reference":[{"key":"S0266466608090099_ref23","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-2545-2"},{"key":"S0266466608090099_ref21","doi-asserted-by":"publisher","DOI":"10.1111\/1468-0262.00151"},{"key":"S0266466608090099_ref20","doi-asserted-by":"publisher","DOI":"10.2307\/2951576"},{"key":"S0266466608090099_ref17","doi-asserted-by":"publisher","DOI":"10.1111\/1468-0262.00459"},{"key":"S0266466608090099_ref9","doi-asserted-by":"publisher","DOI":"10.1080\/00401706.1993.10485033"},{"key":"S0266466608090099_ref5","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-4076(98)00077-3"},{"key":"S0266466608090099_ref4","doi-asserted-by":"crossref","unstructured":"Caner M . (2006) A Pretest and Higher Order Expansions in GMM with Nearly-Weak Instruments. 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(2007) Shrinkage Estimation for Nearly-Singular Designs. Working paper, Department of Statistics, University of Toronto.","DOI":"10.1017\/S0266466608080146"},{"key":"S0266466608090099_ref19","unstructured":"P\u00f6tscher B.M. & Novak A.J. (1994) The Distribution of Estimators after Model Selection: Large and Small Sample Results. 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