Estimating treatment effects for ordered outcomes using maximum simulated likelihood

Abstract

I present four new commands to estimate the effect of a binary endogenous treatment on an ordered outcome. Such models conventionally rely upon joint normality of the unobservables in treatment and outcome processes, as do treatoprobit and switchoprobit. In this article, I highlight the capabilities of treatoprobitsim and switchoprobitsim, which both use a latent-factor structure to model the joint distribution of the treatment and outcome and allow the researcher to relax the assumption of joint normality.