Estimation of Semiparametric Models in the Presence of Endogeneity and Sample Selection

We analyze a semiparametric model for data that suffer from the problems of sample selection, where some of the data are observed for only part of the sample with a probability that depends on a selection equation, and of endogeneity, where a covariate is correlated with the disturbance term. The introduction of nonparametric functions in the model permits great flexibility in the way covariates affect response variables. We present an efficient Bayesian method for the analysis of such models that allows us to consider general systems of outcome variables and endogenous regressors that are continuous, binary, censored, or ordered. Estimation is by Markov chain Monte Carlo (MCMC) methods. The algorithm we propose does not require simulation of the outcomes that are missing due to the selection mechanism, which reduces the computational load and improves the mixing of the MCMC chain. The approach is applied to a model of women’s labor force participation and log-wage determination. Data and computer code used in this article are available online.

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