The endogenous grid method for discrete-continuous dynamic choice models with (or without) 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 grid-point 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 independent and identically distributed extreme value taste shocks that are typically interpreted as “unobserved state variables” in structural econometric applications, or serve as “random noise” to smooth out kinks in the value functions in numerical applications. We present Monte Carlo experiments that demonstrate the reliability and efficiency of the DC-EGM algorithm and the associated maximum likelihood estimator for structural estimation of a life-cycle model of consumption with discrete retirement decisions.