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Sequential Monte Carlo Methods for Estimating Dynamic Microeconomic Models

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Author Info

  • 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.

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File URL: http://www.econ.ohio-state.edu/pdf/blevins/wp11-01.pdf
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Bibliographic Info

Paper provided by Ohio State University, Department of Economics in its series Working Papers with number 11-01.

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Length: 37 pages
Date of creation: May 2011
Date of revision:
Handle: RePEc:osu:osuewp:11-01

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Related research

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

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References

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  1. Susumu Imai & Neelam Jain, 2005. "Bayesian Estimation of Dynamic Discrete Choice Models," 2005 Meeting Papers 432, Society for Economic Dynamics.
  2. Stephen Ryan, 2005. "The Costs of Environmental Regulation in a Concentrated Industry," Working Papers 0510, Massachusetts Institute of Technology, Center for Energy and Environmental Policy Research.
  3. Hu, Yingyao & Shum, Matthew, 2012. "Nonparametric identification of dynamic models with unobserved state variables," Journal of Econometrics, Elsevier, vol. 171(1), pages 32-44.
  4. Victor Aguirregabiria & Pedro Mira, 2004. "Sequential Estimation Of Dynamic Discrete Games," Working Papers wp2004_0413, CEMFI.
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Cited by:
  1. Yingyao Hu & Matthew Shum, 2008. "Identifying Dynamic Games with Serially-Correlated Unobservables," Economics Working Paper Archive 546, The Johns Hopkins University,Department of Economics.
  2. Yingyao Hu & Matthew Shum & Wei Tan, 2010. "A Simple Estimator for Dynamic Models with Serially Correlated Unobservables," Economics Working Paper Archive 558, The Johns Hopkins University,Department of Economics.
  3. Hanming Fang & Edward Kung, 2012. "Why Do Life Insurance Policyholders Lapse? The Roles of Income, Health and Bequest Motive Shocks," NBER Working Papers 17899, National Bureau of Economic Research, Inc.
  4. 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.

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