This paper considers estimation and inference for the binary response model in the case where endogenous variables are included as arguments of the unknown link function. Semiparametric estimators are proposed that avoid the parametric assumptions underlying the likelihood approach as well as...

Replaced with revised version of paper 05/29/04.

A generalized maximum entropy estimator is developed for the linear simultaneous equations systems model. We provide results on large and small sample properties of the estimator. Empirical results illustrate efficiency advantages of the generalized maximum entropy estimator proposed in this...

Information theoretic estimators are specified for a system of linear simultaneous equations, including maximum empirical likelihood, maximum empirical exponential likelihood, and maximum log Euclidean likelihood. Monte Carlo experiments are used to compare finite sample performance of these...

When there is uncertainty concerning the appropriate statistical model to use in representing the data sampling process and corresponding estimators, we consider a basis for optimally combining estimation problems. In the context of the multivariate linear statistical model, we consider a...

This paper introduces a new class of estimators based on minimization of the Cressie-Read (CR) power divergence measure for binary choice models, where neither a parameterized distribution nor a parameterization of the mean is specified explicitly in the statistical model. By incorporating...

This paper presents empirical evidence concerning the finite sample performance of conventional and generalized empirical likelihood-type estimators that utilize instruments in the context of linear structural models characterized by endogenous explanatory variables. There are suggestions in the...

This paper makes three principal contributions. First, we propose a new estimator for the unobservable variable models with endogenous causes. We show that under factor analysis type of assumptions, Robinson and Ferrara (1977)'’s procedure is not fully efficient, and a more efficient procedure...

In this paper we illustrate the use of alternative truncated regression estimators for the general linear model. These include variations of maximum likelihood, Bayesian, and maximum entropy estimators in which the error distributions are doubly truncated. To evaluate the performance of the...