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The Solution and Estimation of Discrete Choice Dynamic Programming Models by Simulation and Interpolation

Monte Carlo Evidence

Bibliographic Data

ID19319608
AuthorsMichael P Keane (0000-0002-3918-1377), Kenneth I Wolpin
Year1994
Volume76
Issue4
Pages648
Publication date1994-11-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueThe Review of Economics and Statistics (JOURNAL)
Journal identifiersISSN: 0034-6535 • E-ISSN: 1530-9142
PublisherJSTOR (PUBLISHER)
DOI10.2307/2109768
OpenAlexW2060268871
LanguageEN
Citations received23
References cited2

Over the past decade, a substantial literature on methods for the estimation of discrete choice dynamic programming (DDP) models of behavior has developed. However, the implementation of these methods can impose major computational burdens because solving for agents' decision rules often involves high dimensional integrations that must be performed at each point in the state space. In this paper we develop an approximate solution method that consists of: (1) using Monte Carlo integration to stimulate the required multiple integrals at a subset of the state points, and (2) interpolating the non-simulated values using a regression function. The overall performance of this approximation method appears to be excellent. Copyright 1994 by MIT Press

Computer graphics (images) · Econometrics · Economics · Estimation · Interpolation (computer graphics) · Markov chain Monte Carlo · Mathematical optimization · Monte Carlo integration · Monte Carlo method · Monte Carlo molecular modeling · Statistics · Applied Mathematics · Computer Science · Consumer Market Behavior and Pricing · Economic and Environmental Valuation · Economics of Agriculture and Food Markets · Mathematics

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Unique citing works23
Citations per year0.79
Citation span1997 - 2023 (27)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 20

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