A Computationally Practical Robust Simulation Estimator for Dynamic Panel Tobit Models

In this paper, a computationally robust simulation estimator is proposed for the dynamic panel Tobit model with large categories of dependence structures. The maximum simulated likelihood estimators are obtained through a recursive algorithm formulated by Geweke-Hajivassiliou-Keane and Gibbs sampling simulators. Monte Carlo experiments indicate that the proposed robust simulation estimators perform well under the errors having a heavy-tailed distribution, even for a small simulation size. The initial conditions problem is also investigated for the robust simulation estimators through Monte Carlo experiments.