Title: Estimation of nonlinear panel models with multiple unobserved effects
Authors: Mingli Chen - University of Warwick (United Kingdom) [presenting]
Abstract: The aim is to propose a fixed effects expectation-maximization (EM) estimator that can be applied to a class of nonlinear panel data models with unobserved heterogeneity, which is modeled as individual effects and/or time effects. Of particular interest is the case of interactive effects, i.e. when the unobserved heterogeneity is modeled as a factor analytical structure. The estimator is obtained through a computationally simple, iterative two-step procedure, where the two steps have closed form solutions. We show that estimator is consistent in large panels and derive the asymptotic distribution for the case of the probit with interactive effects. We develop analytical bias corrections to deal with the incidental parameter problem. Monte Carlo experiments demonstrate that the proposed estimator has good finite-sample properties. We illustrate the use of the proposed model and estimator with an application to international trade networks.