{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T18:33:00Z","timestamp":1790274780751,"version":"4.1.0"},"reference-count":27,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2019,12,23]],"date-time":"2019-12-23T00:00:00Z","timestamp":1577059200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Applied Econometrics"],"published-print":{"date-parts":[[2020,3]]},"abstract":"<jats:title>Summary<\/jats:title><jats:p>This paper considers a panel stochastic production frontier model that allows the dynamic adjustment of technical inefficiency. In particular, we assume that inefficiency follows an AR(1) process. That is, the current year's inefficiency for a firm depends on its past inefficiency plus a transient inefficiency incurred in the current year. Interfirm variations in the transient inefficiency are explained by some firm\u2010specific covariates. We consider four likelihood\u2010based approaches to estimate the model: the full maximum likelihood, pairwise composite likelihood, marginal composite likelihood, and quasi\u2010maximum likelihood approaches. Moreover, we provide Monte Carlo simulation results to examine and compare the finite\u2010sample performances of the four above\u2010mentioned likelihood\u2010based estimators of the parameters. Finally, we provide an empirical application of a panel of 73 Finnish electricity distribution companies observed during 2008\u20132014 to illustrate the working of our proposed models.<\/jats:p>","DOI":"10.1002\/jae.2746","type":"journal-article","created":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T06:25:28Z","timestamp":1577168728000},"page":"217-247","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Estimation of a dynamic stochastic frontier model using likelihood\u2010based approaches"],"prefix":"10.1002","volume":"35","author":[{"given":"Hung\u2010pin","family":"Lai","sequence":"first","affiliation":[{"name":"Department of Economics National Chung Cheng University  Minxiong Township Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Subal C.","family":"Kumbhakar","sequence":"additional","affiliation":[{"name":"Department of Economics SUNY Binghamton  Binghamton New York"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2019,12,23]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/07474930008800482"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/07474938.2013.825126"},{"issue":"2","key":"e_1_2_8_4_1","first-page":"233","article-title":"Pseudolikelihood estimation: Some examples","volume":"53","author":"Arnold B. 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