Hasebe Takuya

In this article, I describe the commands that implement the estimation of three endogenous models of binary choice outcome. The command esbinary fits the endogenously switching model, where a potential outcome differs across two treatment states. The command edbinary fits the endogenous dummy model, which includes a dummy variable indicating the treatment state as one of the explanatory variables. After one estimates the parameters of these models, various treatment effects can be estimated as postestimation statistics. The command ssbinary fits the sample-selection model, where an outcome is observed in only one of the states. The commands fit these models using copula-based maximumlikelihood estimation.

In this article, I describe the escount command, which implements the estimation of an endogenous switching model with count-data outcomes, where a potential outcome differs across two alternate treatment statuses. escount allows for either a Poisson or a negative binomial regression model with lognormal latent heterogeneity. After estimating the parameters of the switching regression model, one can estimate various treatment effects with the command teescount. I also describe the command lncount, which fits the Poisson or negative binomial regression model with lognormal latent heterogeneity.