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Sequential Monte Carlo Methods for Estimating Dynamic Microeconomic Models

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  • Jason R. Blevins

    () (Department of Economics, Ohio State University)

Abstract

This paper develops methods for estimating dynamic structural microeconomic models with serially correlated latent state variables. The proposed estimators are based on sequential Monte Carlo methods, or particle filters, and simultaneously estimate both the structural parameters and the trajectory of the unobserved state variables for each observational unit in the dataset. We focus two important special cases: single agent dynamic discrete choice models and dynamic games of incomplete information. The methods are applicable to both discrete and continuous state space models. We first develop a broad nonlinear state space framework which includes as special cases many dynamic structural models commonly used in applied microeconomics. Next, we discuss the nonlinear filtering problem that arises due to the presence of a latent state variable and show how it can be solved using sequential Monte Carlo methods. We then turn to estimation of the structural parameters and consider two approaches: an extension of the standard full-solution maximum likelihood procedure (Rust, 1987) and an extension of the two-step estimation method of Bajari, Benkard, and Levin (2007), in which the structural parameters are estimated using revealed preference conditions. Finally, we introduce an extension of the classic bus engine replacement model of Rust (1987) and use it both to carry out a series of Monte Carlo experiments and to provide empirical results using the original data.

Suggested Citation

  • Jason R. Blevins, 2011. "Sequential Monte Carlo Methods for Estimating Dynamic Microeconomic Models," Working Papers 11-01, Ohio State University, Department of Economics.
  • Handle: RePEc:osu:osuewp:11-01
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    File URL: http://www.econ.ohio-state.edu/pdf/blevins/wp11-01.pdf
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    References listed on IDEAS

    as
    1. Susumu Imai & Neelam Jain & Andrew Ching, 2009. "Bayesian Estimation of Dynamic Discrete Choice Models," Econometrica, Econometric Society, vol. 77(6), pages 1865-1899, November.
    2. Hu, Yingyao & Shum, Matthew, 2012. "Nonparametric identification of dynamic models with unobserved state variables," Journal of Econometrics, Elsevier, vol. 171(1), pages 32-44.
    3. Victor Aguirregabiria & Pedro Mira, 2007. "Sequential Estimation of Dynamic Discrete Games," Econometrica, Econometric Society, vol. 75(1), pages 1-53, January.
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    Cited by:

    1. Mitsukuni Nishida & Nathan Yang, 2014. "Better Together? Retail Chain Performance Dynamics in Store Expansion Before and After Mergers," Working Papers 14-08, NET Institute.
    2. Edward Kung & Hanming Fang, 2011. "Why Do Life Insurance Policyholders Lapse? The Roles of Income, Health and Bequest Motive Shocks," 2011 Meeting Papers 188, Society for Economic Dynamics.
    3. Arnaud Doucet & Neil Shephard, 2012. "Robust inference on parameters via particle filters and sandwich covariance matrices," Economics Papers 2012-W05, Economics Group, Nuffield College, University of Oxford.
    4. Lux, Thomas, 2017. "Estimation of agent-based models using sequential Monte Carlo methods," Economics Working Papers 2017-07, Christian-Albrechts-University of Kiel, Department of Economics.
    5. Hu Yingyao & Shum Matthew & Tan Wei & Xiao Ruli, 2017. "A Simple Estimator for Dynamic Models with Serially Correlated Unobservables," Journal of Econometric Methods, De Gruyter, vol. 6(1), pages 1-16, January.
    6. repec:eee:dyncon:v:91:y:2018:i:c:p:391-408 is not listed on IDEAS
    7. Yingyao Hu & Matthew Shum, 2008. "Identifying Dynamic Games with Serially-Correlated Unobservables," Economics Working Paper Archive 546, The Johns Hopkins University,Department of Economics.
    8. repec:eee:econom:v:203:y:2018:i:1:p:19-32 is not listed on IDEAS

    More about this item

    Keywords

    dynamic discrete choice; latent state variables; serial correlation; sequential Monte Carlo methods; particle filtering;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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