{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T21:08:37Z","timestamp":1784149717791,"version":"3.55.0"},"reference-count":30,"publisher":"Cambridge University Press (CUP)","issue":"4","license":[{"start":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T00:00:00Z","timestamp":1650844800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Econom. Theory"],"published-print":{"date-parts":[[2023,8]]},"abstract":"<jats:p>In many nonlinear panel data models with fixed effects maximum likelihood estimators suffer from the incidental parameters problem, which often entails that point estimates are markedly biased. While the recent literature has mostly generated methods that yield a first-order bias reduction relative to maximum likelihood, we derive a first- and second-order bias correction of the profile likelihood based on \u201cexpected quantities\u201d which differs from the corresponding correction based on \u201csample averages\u201d derived in Dhaene and Sun (2021,<jats:italic>Journal of Econometrics<\/jats:italic>220, 227\u2013252). While consistency and asymptotic normality of our estimator are derived in a setting where both the number of individuals and the number of time periods grow to infinity, we illustrate in a simulation study that our second-order bias reduction indeed yields an estimator with substantially improved small sample properties relative to its first-order unbiased counterpart, especially when less than 10 time periods are available.<\/jats:p>","DOI":"10.1017\/s0266466622000160","type":"journal-article","created":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T05:22:10Z","timestamp":1650864130000},"page":"693-736","source":"Crossref","is-referenced-by-count":1,"title":["SECOND-ORDER BIAS REDUCTION FOR NONLINEAR PANEL DATA MODELS WITH FIXED EFFECTS BASED ON EXPECTED QUANTITIES"],"prefix":"10.1017","volume":"39","author":[{"given":"Martin","family":"Schumann","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2022,4,25]]},"reference":[{"key":"S0266466622000160_r12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jeconom.2009.02.007"},{"key":"S0266466622000160_r17","doi-asserted-by":"publisher","DOI":"10.1017\/S0266466611000028"},{"key":"S0266466622000160_r16","doi-asserted-by":"publisher","DOI":"10.1111\/1468-0262.00344"},{"key":"S0266466622000160_r14","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-economics-080217-053542"},{"key":"S0266466622000160_r19","doi-asserted-by":"publisher","DOI":"10.1111\/j.1468-0262.2004.00533.x"},{"key":"S0266466622000160_r31","volume-title":"Introductory eEconometrics: A Modern Approach","author":"Wooldridge","year":"2015"},{"key":"S0266466622000160_r27","doi-asserted-by":"crossref","DOI":"10.1093\/oso\/9780198506508.001.0001","volume-title":"Likelihood Methods in Statistics","author":"Severini","year":"2000"},{"key":"S0266466622000160_r9","doi-asserted-by":"publisher","DOI":"10.1093\/restud\/rdv007"},{"key":"S0266466622000160_r1","doi-asserted-by":"publisher","DOI":"10.1111\/1468-0262.00441"},{"key":"S0266466622000160_r18","unstructured":"Hahn, J. , Kuersteiner, G. , & Newey, W. 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