A Simulation Estimator for Dynamic Models of Discrete Choice
AbstractThis paper analyzes a new estimator for the structural parameters of dynamic models of discrete choice. Based on an inversion theorem due to V. J. Hotz and R. Miller (1993), which establishes the existence of a one-to-one mapping between the conditional valuation functions for the dynamic problem and their associated conditional choice probabilities, the authors exploit simulation techniques to estimate models that do not possess terminal states. Drawing on work in empirical process theory by A. Pakes and D. Pollard (1989), they establish its large sample properties and conduct a Monte Carlo study of J. Rust's (1987) model of bus engine replacement to compare its small sample properties with those of maximum likelihood. Copyright 1994 by The Review of Economic Studies Limited.
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Bibliographic InfoPaper provided by Carnegie Mellon University, Tepper School of Business in its series GSIA Working Papers with number 1992-13.
Length: 35 pages
Date of creation: 1992
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 & Robert A. Miller & Seth Sanders & Jeffrey Smith, 1994. "A Simulation Estimator for Dynamic Models of Discrete Choice," Review of Economic Studies, Wiley Blackwell, vol. 61(2), pages 265-89, April.
- V. Joseph Hotz & Robert A. Miller & Seth Sanders & Jeffrey Smith, 1992. "A Simulation Estimator for Dynamic Models of Discrete Choice," Working Papers 9205, Harris School of Public Policy Studies, University of Chicago.
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