Conditional Choice Probability Estimation of Dynamic Discrete Choice Models With Unobserved Heterogeneity

We adapt the expectation-maximization algorithm to incorporate unobserved heterogeneity into conditional choice probability (CCP) estimators of dynamic discrete choice problems. The unobserved heterogeneity can be time-invariant or follow a Markov chain. By developing a class of problems where the d...

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Veröffentlicht in:	Econometrica Jg. 79; H. 6; S. 1823 - 1867
Hauptverfasser:	Arcidiacono, Peter, Miller, Robert A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford, UK Blackwell Publishing Ltd 01.11.2011 Econometric Society Wiley-Blackwell
Schlagworte:	Algorithms Applications Computational methods Conditional probabilities Decision making models Discrete choice Discrete choice models Diskrete Entscheidung Distribution theory Dynamic discrete choice Dynamic modeling Dynamische Wirtschaftstheorie Dynamisches Spiel Econometrics Economic models Engines Estimating techniques Estimation Estimation methods Estimators Exact sciences and technology Future value Heterogeneity Information and communication technologies Insurance, economics, finance Markov analysis Markov processes Mathematics Model testing Monte Carlo simulation Numerical analysis Numerical analysis. Scientific computation Numerical methods in probability and statistics Optimization Optimization algorithms Parametric models Preliminary estimates Probability Probability and statistics Probability theory and stochastic processes Schätztheorie Sciences and techniques of general use Statistical methods Statistics Studies Theorie unobserved heterogeneity Working papers Invariant Monte Carlo method Dynamic discrete choice Conditional distribution Maximization Statistical distribution Estimation Optimization method Conditional probability Stochastic method Algorithm Markov chain Statistical method Heterogeneity Numerical analysis Discrete choice unobserved heterogeneity Approximation theory Econometrics Dynamic model
ISSN:	0012-9682, 1468-0262
Online-Zugang:	Volltext
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Zusammenfassung:	We adapt the expectation-maximization algorithm to incorporate unobserved heterogeneity into conditional choice probability (CCP) estimators of dynamic discrete choice problems. The unobserved heterogeneity can be time-invariant or follow a Markov chain. By developing a class of problems where the difference in future value terms depends on a few conditional choice probabilities, we extend the class of dynamic optimization problems where CCP estimators provide a computationally cheap alternative to full solution methods. Monte Carlo results confirm that our algorithms perform quite well, both in terms of computational time and in the precision of the parameter estimates.
Bibliographie:	istex:9B539F799481D4DE98460AC6F2589DEE41E14AA0 ark:/67375/WNG-DB9LHVCJ-M ArticleID:ECTA1167 We thank Victor Aguirregabiria, Esteban Aucejo, Lanier Benkard, Jason Blevins, Paul Ellickson, George‐Levi Gayle, Joe Hotz, Pedro Mira, three anonymous referees, and the co‐editor for their comments. We have benefited from seminars at UC Berkeley, Duke University, University College London, University of North Carolina, Northwestern University, The Ohio State University, University of Pennsylvania, University of Rochester, Stanford University, University of Texas, Vanderbilt University, University of Virginia, Washington University, University of Wisconsin, IZA, Microeconometrics Conferences at the Cowles Foundation, the MOVE Conference at Universitat Autònoma de Barcelona, and the NASM of the Econometric Society. Andrew Beauchamp, Jon James, and Josh Kinsler provided excellent research assistance. Financial support was provided for by NSF Grants SES‐0721059 and SES‐0721098. SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23
ISSN:	0012-9682 1468-0262
DOI:	10.3982/ECTA7743