A Monte Carlo EM method for estimating multinomial probit models
We develop a framework to perform maximum likelihood estimation in the multinomial probit model using a Monte Carlo EM algorithm. Our method includes a Gibbs step. This approach is different from likelihood procedures currently in use for this class of models, in that it does not involve direct evaluation and maximization of the observed data likelihood. Instead, we take advantage of the underlying continuum to simplify calculations. We also develop extensions of this Monte Carlo EM method for analyzing multi-period data. The computations are illustrated through real and simulated data.
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