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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| Published in | Journal of applied econometrics (Chichester, England) Vol. 31; no. 5; pp. 773 - 804 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
Chichester
Blackwell Publishing Ltd
01.08.2016
Wiley (Variant) Wiley-Blackwell Wiley Periodicals Inc |
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| Abstract | 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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| AbstractList | 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. Copyright © 2015 John Wiley & Sons, Ltd. 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. Copyright © 2015 John Wiley & Sons, Ltd. 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. Copyright 2015 John Wiley & Sons, Ltd. Copyright John Wiley & Sons. Reproduced with Permission. An electronic version of this article is available online at http://www.interscience.wiley.com 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. 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. Copyright © 2015John Wiley & Sons, Ltd. |
| Author | Blevins, Jason R. |
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| BackLink | http://www.econis.eu/PPNSET?PPN=887346774$$DView this record in ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften |
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Sequential estimation of dynamic discrete games. Econometrica 75: 1-53. Stinebrickner TR. 2000. Serially correlated variables in dynamic, discrete choice models. Journal of Applied Econometrics 15: 595-624. Hotz VJ, Miller RA. 1993. Conditional choice probabilities and the estimation of dynamic models. Review of Economic Studies 60: 497-529. Creal D. 2012. A survey of sequential Monte Carlo methods for economics and finance. Econometric Reviews 31: 245-296. Norets A. 2009b. Inference in dynamic discrete choice models with serially correlated unobserved state variables. Econometrica 77: 1665-1682. Flury T, Shephard N. 2011. Bayesian inference based only on simulated likelihood: particle filter analysis of dynamic economic models. Econometric Theory 27: 933-956. Cappé O, Moulines E, Ryden T. 2005. Inference in Hidden Markov Models, Springer: New York. Imai S, Jain N, Ching A. 2009. Bayesian estimation of dynamic discrete choice models. Econometrica 77: 1865-1899. 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Identifying dynamic games with serially-correlated unobservables. In Advances in Econometrics 31: 97-113. Judd KL. 1998. Numerical Methods in Economics, MIT Press: Cambridge, MA. Hu Y, Shum M. 2012. Nonparametric identification of dynamic models with unobserved state variables. Journal of Econometrics 171: 32-44. Norets A. 2012. Estimation of dynamic discrete choice models using artificial neural network approximations. Econometric Reviews 31: 84-106. Gordon N, Salmond D, Smith A. 1993. Novel approach to nonlinear/non-gaussian Bayesian state estimation. IEEE Proceedings F: Radar and Signal Processing 140: 107-113. Kalman RE. 1960. A new approach to linear filtering and prediction problems. Transactions of the ASME-Journal of Basic Engineering 82: 35-45. Ryan S. 2012. The costs of environmental regulation in a concentrated industry. Econometrica 80: 1019-1061. Liu J. 2001. Monte Carlo Strategies in Scientific Computing, Springer: New York. Norets A. 2010. 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| References_xml | – reference: Whiteley N. 2012. Sequential Monte Carlo samplers: error bounds and insensitivity to initial conditions. Stochastic Analysis and Applications 30: 774-798. – reference: Cappé O, Moulines E, Ryden T. 2005. Inference in Hidden Markov Models, Springer: New York. – reference: Blevins JR. 2014. Nonparametric identification of dynamic decision processes with discrete and continuous choices. Quantitative Economics 5: 531-554. – reference: Pakes A. 1986. Patents as options: some estimates of the value of holding European patent stocks. Econometrica 54: 755-784. – reference: Gordon N, Salmond D, Smith A. 1993. Novel approach to nonlinear/non-gaussian Bayesian state estimation. IEEE Proceedings F: Radar and Signal Processing 140: 107-113. – reference: van der Vaart AW, Wellner JA. 1996. Weak Convergence and Empirical Processes, Springer: New York. – reference: Judd KL. 1998. Numerical Methods in Economics, MIT Press: Cambridge, MA. – reference: Pitt MK, Shephard N. 1999. Filtering via simulation: auxiliary particle filters. Journal of the American Statistical Association 94: 590-591. – reference: A Doucet, N de Freitas, and N Gordon (eds). 2001. Sequential Monte Carlo Methods in Practice Springer: New York. – reference: Creal D. 2012. A survey of sequential Monte Carlo methods for economics and finance. Econometric Reviews 31: 245-296. – reference: Timmins C. 2002. Measuring the dynamic efficiency costs of regulators preferences: municipal water utilities in the arid West. Econometrica 70: 603-629. – reference: Arcidiacono P, Miller RA. 2011. Conditional choice probability estimation of dynamic discrete choice models with unobserved heterogeneity. Econometrica 79: 1823-1867. – reference: Stinebrickner TR. 2000. Serially correlated variables in dynamic, discrete choice models. Journal of Applied Econometrics 15: 595-624. – reference: Kalman RE. 1960. A new approach to linear filtering and prediction problems. Transactions of the ASME-Journal of Basic Engineering 82: 35-45. – reference: Ryan S. 2012. The costs of environmental regulation in a concentrated industry. Econometrica 80: 1019-1061. – reference: Aguirregabiria V, Mira P. 2010. Dynamic discrete choice structural models: a survey. Journal of Econometrics 156: 38-67. – reference: Hotz VJ, Miller RA. 1993. Conditional choice probabilities and the estimation of dynamic models. Review of Economic Studies 60: 497-529. – reference: Norets A. 2010. Continuity and differentiability of expected value functions in dynamic discrete choice models. Quantitative Economics 1: 305-322. – reference: Rust J. 1987. Optimal replacement of GMC bus engines: an empirical model of Harold Zurcher. Econometrica 55: 999-1013. – reference: Norets A. 2009b. Inference in dynamic discrete choice models with serially correlated unobserved state variables. Econometrica 77: 1665-1682. – reference: Imai S, Jain N, Ching A. 2009. Bayesian estimation of dynamic discrete choice models. Econometrica 77: 1865-1899. – reference: Liu J. 2001. Monte Carlo Strategies in Scientific Computing, Springer: New York. – reference: Pakes A, Pollard D. 1989. Simulation and the asymptotics of optimization estimators. Econometrica 57: 1027-1057. – reference: Heckman JJ. 1979. Sample selection bias as a specification error. Econometrica 47: 153-161. – reference: Norets A. 2012. Estimation of dynamic discrete choice models using artificial neural network approximations. Econometric Reviews 31: 84-106. – reference: Hu Y, Shum M. 2013. Identifying dynamic games with serially-correlated unobservables. In Advances in Econometrics 31: 97-113. – reference: Del Moral P, Ledoux M. 2000. Convergence of empirical processes for interacting particle systems with applications to nonlinear filtering. Journal of Theoretical Probability 13: 225-257. – reference: Flury T, Shephard N. 2011. Bayesian inference based only on simulated likelihood: particle filter analysis of dynamic economic models. Econometric Theory 27: 933-956. – reference: Su CL, Judd KL.2012. Constrained optimization approaches to estimation of structural models. Econometrica 80: 2213-2230. – reference: Keane M, Wolpin KI.. The solution and estimation of discrete choice dynamic programming models by simulation and interpolation: Monte Carlo evidence. Review of Economics and Statistics 76 1994: 648-672. – reference: Eckstein Z, Wolpin KI. 1989. The specification and estimation of dynamic stochastic discrete choice models: a survey. Journal of Human Resources 24: 562-598. – reference: Fernández-Villaverde J, Rubio-Ramírez JF. 2007. Estimating macroeconomic models: a likelihood approach. Review of Economic Studies 74: 1059-1087. – reference: Aguirregabiria V, Mira P. 2007. 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| Title | Sequential Monte Carlo Methods for Estimating Dynamic Microeconomic Models |
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