Simulation Based Inference for Dynamic Multinomial Choice Models

Our goal in this chapter is to explain concretely how to implement simulation methods in a very general class of models that are extremely useful in applied work: dynamic discrete choice models where one has available a panel of multinomial choice histories and partially observed payoffs. Moreover, the techniques we describe are directly applicable to a general class of models that includes static discrete choice models, the Heckman (1976) selection model, and all of the Heckman (1981) models (such as static and dynamic Bernoulli models, Markov models, and renewal processes.) The particular procedure that we describe derives from a suggestion by Geweke and Keane (1999a), and has the advantages that it does not require the econometrician to solve the agents’ dynamic optimization problem, or to make strong assumptions about the way individuals form expectations.