A Simulation Estimator for Dynamic Models of Discrete Choice
This 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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