Conditional Choice Probabilities and the Estimation of Dynamic Models
AbstractThis paper develops a new method for estimating the structural parameters of dynamic programming problems in which choices are discrete. The method reduces the computational burden of estimating such models. We show the valuation functions characterizing the expected future utility function associated with such 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 on the relative frequencies of observed choices using observations with the same (or similar) state variables. Substituting the estimators for the true conditional choice probabilities in formulating optimal decision rules, we establish the consistency and asymptotic normality of the resulting structural parameter estimators. To illustrate our new method, we estimate a dynamic model of parental contraceptive choice and fertility using data from the National Fertility Survey.
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Bibliographic InfoPaper provided by Carnegie Mellon University, Tepper School of Business in its series GSIA Working Papers with number 1992-12.
Length: 46 pages
Date of creation: 1991
Date of revision:
Contact details of provider:
Postal: Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213-3890
Web page: http://www.tepper.cmu.edu/
Other versions of this item:
- Hotz, V Joseph & Miller, Robert A, 1993. "Conditional Choice Probabilities and the Estimation of Dynamic Models," Review of Economic Studies, Wiley Blackwell, vol. 60(3), pages 497-529, July.
- V. Joseph Hotz & Robert A. Miller, 1992. "Conditional Choice Probabilities and the Estimation of Dynamic Models," Working Papers 9202, Harris School of Public Policy Studies, University of Chicago.
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