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Identifying Dynamic Games with Serially-Correlated Unobservables

  • Yingyao Hu
  • Matthew Shum

In this paper we consider the nonparametric identification of Markov dynamic games models in which each firm has its own unobserved state variable, which is persistent over time. This class of models includes most models in the Ericson and Pakes (1995) and Pakes and McGuire (1994) framework. We provide conditions under which the joint Markov equilibrium process of the firms' observed and unobserved variables can be nonparametrically identified from data. For stationary continuous action games, we show that only three observations of the observed component are required to identify the equilibrium Markov process of the dynamic game. When agents?choice variables are discrete, but the unobserved state variables are continuous, four observations are required.

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Paper provided by The Johns Hopkins University,Department of Economics in its series Economics Working Paper Archive with number 546.

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Date of creation: Oct 2008
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Handle: RePEc:jhu:papers:546
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  1. James J. Heckman & Salvador Navarro, 2005. "Dynamic Discrete Choice and Dynamic Treatment Effects," NBER Technical Working Papers 0316, National Bureau of Economic Research, Inc.
  2. Patrick Bajari & C. Lanier Benkard & Jonathan Levin, 2004. "Estimating Dynamic Models of Imperfect Competition," NBER Working Papers 10450, National Bureau of Economic Research, Inc.
  3. Yingyao Hu & Susanne M. Schennach, 2008. "Instrumental Variable Treatment of Nonclassical Measurement Error Models," Econometrica, Econometric Society, vol. 76(1), pages 195-216, 01.
  4. Ariel Pakes & Michael Ostrovsky & Steve Berry, 2004. "Simple Estimators for the Parameters of Discrete Dynamic Games (with Entry/Exit Examples)," Harvard Institute of Economic Research Working Papers 2036, Harvard - Institute of Economic Research.
  5. Hotz, V.J. & Miller, R.A., 1991. "Conditional Choice Probabilities and the Estimation of Dynamic Models," GSIA Working Papers 1992-12, Carnegie Mellon University, Tepper School of Business.
  6. Ackerberg, Daniel & Lanier Benkard, C. & Berry, Steven & Pakes, Ariel, 2007. "Econometric Tools for Analyzing Market Outcomes," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 63 Elsevier.
  7. Jason R. Blevins, 2011. "Sequential Monte Carlo Methods for Estimating Dynamic Microeconomic Models," Working Papers 11-01, Ohio State University, Department of Economics.
  8. Flavio Cunha & James Heckman & Susanne Schennach, 2010. "Estimating the Technology of Cognitive and Noncognitive Skill Formation," NBER Working Papers 15664, National Bureau of Economic Research, Inc.
  9. Hu, Yingyao & Shum, Matthew, 2012. "Nonparametric identification of dynamic models with unobserved state variables," Journal of Econometrics, Elsevier, vol. 171(1), pages 32-44.
  10. Ericson, Richard & Pakes, Ariel, 1995. "Markov-Perfect Industry Dynamics: A Framework for Empirical Work," Review of Economic Studies, Wiley Blackwell, vol. 62(1), pages 53-82, January.
  11. Martin Pesendorfer & Philipp Schmidt-Dengler, 2008. "Asymptotic Least Squares Estimators for Dynamic Games -super-1," Review of Economic Studies, Oxford University Press, vol. 75(3), pages 901-928.
  12. Thierry Magnac & David Thesmar, 2002. "Identifying Dynamic Discrete Decision Processes," Econometrica, Econometric Society, vol. 70(2), pages 801-816, March.
  13. Victor Aguirregabiria & Pedro Mira, 2004. "Sequential Estimation Of Dynamic Discrete Games," Working Papers wp2004_0413, CEMFI.
  14. Abbring, Jaap H. & Heckman, James J., 2007. "Econometric Evaluation of Social Programs, Part III: Distributional Treatment Effects, Dynamic Treatment Effects, Dynamic Discrete Choice, and General Equilibrium Policy Evaluation," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 72 Elsevier.
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