Estimation of ordered probit model with endogenous switching between two latent regimes

Ordinal responses can be generated, in the time-series context, by different latent regimes or, in the cross-sectional context, by different unobserved groups of population. These latent classes or states can distort the inference in a traditional single-equation model. Finite mixture or regime switching models surmount the problem of unobserved heterogeneity or clustering through their flexible form. The available Stata command for finite mixture of ordered probit models, fmm: oprobit, does not allow for endogenous switching, when the unobservables in the switching equation are correlated with the unobservables in the outcome equations. We introduce two new commands, swopit and swopitc, that fit a switching ordered probit model for ordered choices with exogenous and endogenous switching between two unobserved regimes or groups. We provide a battery of postestimation commands, access the small-sample performance of the maximum likelihood estimator of the parameters and the bootstrap estimator of standard errors by Monte Carlo experiments, and apply the new commands to model the policy interest rates and health status responses.