We examine instrumental variables estimation in situations where the instrument is only observed for a sub-sample, which is fairly common in empirical research. Typically, researchers simply limit the analysis to the sub-sample where the instrument is non-missing. We show that when the...

We examine instrumental variables estimation in situations where the instrument is only observed for a sub-sample, which is fairly common in empirical research. Typically, researchers simply limit the analysis to the sub-sample where the instrument is non-missing. We show that when the...

We address the estimation of sample selection and endogenous treatment models with social interactions. To model the interaction between individuals in an internally consistent matter we employ a game theoretic approach based on the use of a discrete Bayesian game. We overcome the substantial...

We examine instrumental variables estimation in situations where the instrument is only observed for a sub-sample, which is fairly common in empirical research. Typically, researchers simply limit the analysis to the sub-sample where the instrument is non-missing. We show that when the...

To analyze data obtained by non-random sampling in the presence of cross-sectional dependence, estimation of a sample selection model with a spatial lag of a latent dependent variable or a spatial error in both the selection and outcome equations is considered. Since there is no estimation...

The aim of the paper is to relax distributional assumptions on the error terms, often imposed in parametric sample selection models to estimate causal effects, when plausible exclusion restrictions are not available. Within the principal stratification framework, we approximate the true...

We consider a general sample selection model where unit and item nonresponse simultaneously affect a regression relationship of interest, and both types of nonresponse are potentially correlated. We estimate both parametric and semiparametric specifications of the model. The parametric...

The aim of the paper is to relax distributional assumptions on the error terms, often imposed in parametric sample selection models to estimate causal effects, when plausible exclusion restrictions are not available. Within the principal stratification framework, we approximate the true...

The control function in the semiparametric selection model is zero at infinity. This paper proposes additional restrictions of the same type and shows how to use them to test assumed exclusion restrictions necessary for root N estimation of the model. The test is based on the estimated control...

We address the estimation of sample selection and endogenous treatment models with social interactions. To model the interaction between individuals in an internally consistent matter we employ a game theoretic approach based on the use of a discrete Bayesian game. We overcome the substantial...