Classical and Bayesian Analysis of a Probit Panel Data Model with Unobserved Individual Heterogeneity and Autocorrelated Errors

In this paper, we perform both classical and Bayesian analysis of a panel probit model with unobserved individual heterogeneity and serially correlated errors. In the classical part, we use E¢ cient Importance Sampling (EIS) in evaluating a sequentially factorized simulated maximum likelihood function. In the Bayesian part, we utilize the Markov Chain Monte Carlo (MCMC) sampling scheme, augmenting the data with latent variables. We sample the unobserved individual heterogeneity component as one Gibbs block drawing from a piece-wise linear approximation to the marginal posterior density constructed with a nonparametric form of EIS.

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