Sequential Monte Carlo Methods for Estimating Dynamic Microeconomic Models

This paper develops estimators for dynamic microeconomic models with serially correlated unobserved state variables using sequential Monte Carlo methods to estimate the parameters and the distribution of the unobservables. If persistent unobservables are ignored, the estimates can be subject to a dy...

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Bibliographic Details
Published inJournal of applied econometrics (Chichester, England) Vol. 31; no. 5; pp. 773 - 804
Main Author Blevins, Jason R.
Format Journal Article
LanguageEnglish
Published Chichester Blackwell Publishing Ltd 01.08.2016
Wiley (Variant)
Wiley-Blackwell
Wiley Periodicals Inc
Subjects
Economic models
Microeconomics
Monte Carlo simulation
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Summary:This paper develops estimators for dynamic microeconomic models with serially correlated unobserved state variables using sequential Monte Carlo methods to estimate the parameters and the distribution of the unobservables. If persistent unobservables are ignored, the estimates can be subject to a dynamic form of sample selection bias. We focus on single-agent dynamic discrete-choice models and dynamic games of incomplete information. We propose a full-solution maximum likelihood procedure and a two-step method and use them to estimate an extended version of the capital replacement model of Rust with the original data and in a Monte Carlo study.
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ISSN:0883-7252
1099-1255
DOI:10.1002/jae.2470