@article{f57b9555c9b44eff9c167d594776132c,
title = "Orthogonality conditions for Tobit models with fixed effects and lagged dependent variables",
abstract = "This paper presents orthogonality conditions for censored regression models with fixed effects and lagged dependent variables. The orthogonality conditions can be used to construct method of moments estimators of the parameters of the model. Nonlinear fixed effects models are usually estimated by maximum likelihood, with the fixed effects treated as parameters to be estimated. Monte Carlo results indicate that in a Tobit model with fixed effects and lagged dependent variables, the maximum likelihood estimator of the effect of the lagged dependent variable performs poorly. The method of moments estimator based on the orthogonality conditions presented here, however performs quite well.",
author = "Honor{\'e}, {Bo E.}",
note = "Funding Information: Correspondence to: Bo E. Honor& Department of Economics, Northwestern University, Evanston, IL 60208, USA. *This research was supported by NSF Grants No. 8809352 and 9009879. I am thankful to Jim Heckman and Chuck Manski for asking questions which inspired this research, and to Jeff Campbell, Tom Downes, Ekaterini Kyriazidou, Franc0 Peracchi, Rob Porter, and two anonymous referees for suggestions and comments. Jeff Campbell also provided excellent research assistance. {\textquoteright} For some nonlinear models, it is possible to use conditional likelihood functions to deal with the fixed effects [see, for example, Chamberlain (1985)]. See also Maddala (1987). Manski (1987) constructed a maximum score estimator of the discrete choice model with fixed effects. Copyright: Copyright 2014 Elsevier B.V., All rights reserved.",
year = "1993",
month = sep,
doi = "10.1016/0304-4076(93)90038-7",
language = "English (US)",
volume = "59",
pages = "35--61",
journal = "Journal of Econometrics",
issn = "0304-4076",
publisher = "Elsevier B.V.",
number = "1-2",
}