Pairwise Difference Estimation of Nonlinear Models
James Powell, UC Berkeley
Bo Honoré, Princeton University
Abstract
This paper uses insights from the literature on estimation of nonlinear
panel data models to construct estimators of a number of semiparametric
models with a partially linear index, including the partially linear
logit model, the partially linear censored regression model, and the
censored regression model with selection.. We develop the relevant
asymptotic theory for these estimators and we apply the theory to derive
the asymptotic distribution of the estimator for the partially linear
logit model. We evaluate the finite sample behavior of this estimator
using a Monte Carlo study.