Semiparametric estimation of nonlinear panel-data models
07 / 2011 - 07 / 2015
The use of panel data in economic applications steadily increases, but there is limited knowledge regarding estimation of nonlinear fixed-effect models. Most estimators are designed for particular models and analogs of general semiparametric estimation available for cross-sectional data do not practically exist. To improve upon this, a method for correcting bias of semiparametric estimators is first proposed, based on the indirect inference principle. Next, this bias correction technique is used to adapt existing semiparametric estimators of single-index models to nonlinear fixed-effect models. Finally, a new rank-correlation estimator is proposed for panel models with limited dependent response variables.