Computational Issues in the Sequential Probit Model : A Monte Carlo Study

We discuss computational issues in the sequential probit model that have limited its use in applied research. We estimate parameters of the model by the method of simulated likelihood and by Bayesian MCMC algorithms. We provide Monte Carlo evidence on the relative performance of both estimators. Given the numerical difficulties associated with the estimation procedures, we advise the applied researcher to use both stochastic optimization algorithms in the Simulated Maximum Likelihood approach as well as Bayesian MCMC algorithms to check the compatibility of the results