Estimating Discrete-Continuous Choice Models : The Endogenous Grid Method with Taste Shocks

We present a fast and accurate computational method for solving and estimating a class of dynamic programming models with discrete and continuous choice variables. The solution method we develop for structural estimation extends the endogenous gridpoint method (EGM) to discrete-continuous (DC) problems. Discrete choices can lead to kinks in the value functions and discontinuities in the optimal policy rules, greatly complicating the solution of the model. We show how these problems are ameliorated in the presence of additive choice-specific IID extreme value taste shocks. We present Monte Carlo experiments that demonstrate the reliability and efficiency of the DC-EGM and the associated Maximum Likelihood estimator for structural estimation of a life cycle model of consumption with discrete retirement decisions