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.
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