{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T12:50:38Z","timestamp":1763643038671},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2020,10,12]],"date-time":"2020-10-12T00:00:00Z","timestamp":1602460800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,10]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>Linear generalised method of moments (GMM) estimators for dynamic panel models with predetermined or endogenous regressors suffer from a weak instruments problem when the data are highly persistent. In this paper, we propose new random- and fixed-effects limited-information quasi\u2013maximum likelihood estimators (LIQMLEs) for such models. We also discuss LIQMLEs for models that contain time-varying individual effects. Unlike system GMM estimators, the LIQMLEs do not require mean stationarity conditions for consistency. Such conditions often do not hold for the models we consider. Our LIQMLEs are based on a two-step control function approach that includes the first-stage model residuals for a predetermined or endogenous regressor in the outcome equation. The LIMLEs are more precise than nonlinear GMM estimators that are based on the original outcome equation. The LIQMLEs also compare favourably to various alternative (quasi\u2013) maximum likelihood estimators in terms of precision, robustness, and\/or ease of computation.<\/jats:p>","DOI":"10.1093\/ectj\/utaa036","type":"journal-article","created":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T20:32:33Z","timestamp":1607632353000},"page":"417-441","source":"Crossref","is-referenced-by-count":2,"title":["Identification without assuming mean stationarity: quasi\u2013maximum likelihood estimation of dynamic panel models with endogenous regressors"],"prefix":"10.1093","volume":"24","author":[{"given":"Hugo","family":"Kruiniger","sequence":"first","affiliation":[{"name":"Department of Economics and Finance, Durham University, 1 Mill Hill Lane, Durham, DH1 3LB, 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