ssivqreg: quantile sample selection models with endogenous regressors in Stata

We introduce ssivqreg, a new Stata estimation command that enables researchers to estimate quantile sample selection models in the presence of both endogenous and exogenous regressors. This command extends existing Stata implementations of quantile selection models in several important ways. First, it accommodates endogenous regressors, a feature not available in earlier implementations. Second, while bootstrapped standard errors are supported, ssivqreg also provides analytical asymptotic standard errors, significantly reducing computation time. Third, the command offers two estimation approaches: one based on profiled generalized method of moments (GMM), and another that estimates model parameters by smoothing the estimating equations. To our knowledge, this smoothing technique has not previously been applied in the context of quantile selection models. It is applicable regardless of whether regressors are endogenous or exogenous and offers clear advantages in terms of computational efficiency. We demonstrate the capabilities of ssivqreg through a simulation study and an empirical application.