THE SOLUTION AND ESTIMATION OF DISCRETE-CHOICE DYNAMIC-PROGRAMMING MODELS BY SIMULATION AND INTERPOLATION - MONTE-CARLO EVIDENCE

Over the past decade, a substantial literature on methods for the esti mation of discrete choice dynamic programming (DDP) models of behavior has developed. However, the implementation of these methods can impos e major computational burdens because solving for agents' decision rul es often involves high dimensional integrations that must be performed at each point in the state space. In this paper we develop an approxi mate solution method that consists of: 1) using Monte Carlo integratio n to stimulate the required multiple integrals at a subset of the stat e points, and 2) interpolating the non-simulated values using a regres sion function. The overall performance of this approximation method ap pears to be excellent.