Consideration of latent heterogeneity is of special importance in non linear models for gauging correctly the effect of explaining variables on the dependent variable. This paper adopts the stratified model-based clustering approach for modeling latent heterogeneity for panel probit models....

A common approach to dealing with missing data is to estimate the model on the common subset of data, by necessity throwing away potentially useful data. We derive a new probit type estimator for models with missing covariate data where the dependent variable is binary. For the benchmark case of...

In non-linear regression models, such as the heteroskedastic probit model, coefficients cannot be interpreted as marginal effects. Marginal effects can be computed as a non-linear combination of the regression coefficients. Standard errors of the marginal effects needed for inference and...

Dagenais (1999) and Lucchetti (2002) have demonstrated that the naive GMM estimator of Grogger (1990) for the probit model with an endogenous regressor is not consistent. This paper completes their discussion by explaining the reason for the inconsistency and presenting a natural solution....