Conditional Choice Probabilities and the Estimation of Dynamic Models.
This paper develops a new method for estimating the structural parameters
of (discrete choice) dynamic programming problems. They show the valuation
functions characterizing the expected future utility associated with the
choices often can be represented as an easily computed function of the
state variables, structural parameters, and the probabilities of choosing
alternative actions for states which are feasible in the future. Under
certain conditions, nonparametric estimators of these probabilities can be
formed from sample information. Substituting the estimators for the true
conditional choice probabilities, the authors establish the consistency
and asymptotic normality of the resulting structural parameter estimators. Copyright 1993 by The Review of Economic Studies Limited.