A smooth likelihood simulator for dynamic disequilibrium models

This article considers the simulation of likelihood functions for dynamic disequilibrium models without sample separation information. A recursive simulation algorithm is proposed. The recursive simulation algorithm is computationally tractable for a class of dynamic disequilibrium models and serially correlated disturbance models. The simulated likelihood functions are smooth in parameters. Monte Carlo studies are provided to demonstrate the computational efficiency of this approach and investigate finite sample properties of simulated likelihood estimators and likelihood ratio test statistics. Regime classification rules based on simulated likelihood are introduced. Finite sample results of the simulated likelihood approach are encouraging.