Estimating binary choice models with endogeneity is of considerable importance in microeconometrics. The leading control function approach does not apply when the endogenous variable is binary. We propose a multi-stage estimation procedure for a heteroscedastic binary choice model with an...

Using new estimation methods and data, I find that Catholic schooling substantially increases years of schooling by 0.42 to 0.47. The estimates are robust to various specifications that account for potential selection bias.

Many randomized controlled trials require participants to opt in. Such self-selection could introduce a potential bias, because only the most optimistic may participate. We revisit this prediction. We argue that in many situations, the experimental intervention is competing with alternative...

This paper discusses the copula-based approach of a bivariate binary choice model. We derive the marginal effects of explanatory variables on an outcome of interest (both direct and indirect) in the model. We also show that the signs of the marginal effects are determined by the signs of the...

This paper deals with the question whether exclusion restrictions on the exogenous regressors are necessary to identify two equation probit models with endogenous dummy regressor. We show that Wilde (2000)’s criterion is insufficient for (point) identification.

This paper evaluates the performance of a recently emerging multivariate quadrature-based Sparse Grids Integration (SGI) and the well-known Geweke–Hajivassiliou–Keane (GHK) simulator in estimating multivariate binary probit models. Monte Carlo exercises demonstrate that in lower dimension...

This study examines the relationship between technological change and a series of key macroeconomic variables in G7 countries. Basically, we find that the rate of interest has a negative impact on the pace of innovation, while investments in the R&D sector, high-technology exports, as well as...

This study applies an estimator based on inequality restrictions to multivariate discrete choice models and identifies the conditions under which the inequality estimator performs well in small samples and for complex problems.

We present an alternative explanation of the logit probabilistic choice from the equal likelihood hypothesis without the Gumbel distribution. The hypothesis is that if the total utility values from combinations of actions are the same, all such combinations of actions are equally likely.

We propose an alternative bivariate zero-inflated negative binomial (BZINB) regression model based on a copula. The empirical result shows that the proposed model performs better than the existing BZINB models in terms of the maximum log-likelihood and the AIC.