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Nonparametric Identification of Dynamic Models with Unobserved State Variables

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  • Yingyao Hu
  • Matthew Shum

Abstract

We consider the identification of a Markov process {Wt,Xt*} for t = 1, 2, ... , T when only {Wt} for t = 1, 2, ... , T is observed. In structural dynamic models, Wt denotes the sequence of choice variables and observed state variables of an optimizing agent, while Xt* denotes the sequence of serially correlated unobserved state variables. The Markov setting allows the distribution of the unobserved state variable Xt* to depend on Wt-1 and Xt-1*. We show that the joint distribution f Wt, Xt*, Wt-1, Xt-1* is identified from the observed distribution f Wt+1, Wt, Wt-1, Wt-2, Wt-3 under reasonable assumptions. Identification of f Wt, Xt*, Wt-1, Xt-1* is a crucial input in methodologies for estimating dynamic models based on the "conditional-choice-probability (CCP)" approach pioneered by Hotz and Miller.

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Paper provided by The Johns Hopkins University,Department of Economics in its series Economics Working Paper Archive with number 543.

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Date of creation: Apr 2008
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Handle: RePEc:jhu:papers:543

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Citations

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Cited by:
  1. Yingyao Hu & Matthew Shum, 2008. "Nonparametric Identification of Dynamic Models with Unobserved State Variables," Economics Working Paper Archive 543, The Johns Hopkins University,Department of Economics.
  2. Yingyao Hu & Yutaka Kayaba & Matt Shum, 2010. "Nonparametric learning rules from bandit experiments: the eyes have it!," CeMMAP working papers CWP15/10, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  3. 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.
  4. 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.
  5. Zhou, Yiyi, 2012. "Failure to Launch in Two-Sided Markets: A Study of the U.S. Video Game Market," MPRA Paper 42002, University Library of Munich, Germany.
  6. Yingyao Hu & Matthew Shum, 2008. "Identifying Dynamic Games with Serially-Correlated Unobservables," Economics Working Paper Archive 546, The Johns Hopkins University,Department of Economics.
  7. Patrick Bajari & Chenghuan Sean Chu & Denis Nekipelov & Minjung Park, 2013. "A Dynamic Model of Subprime Mortgage Default: Estimation and Policy Implications," NBER Working Papers 18850, National Bureau of Economic Research, Inc.
  8. Jason R. Blevins, 2011. "Sequential Monte Carlo Methods for Estimating Dynamic Microeconomic Models," Working Papers 11-01, Ohio State University, Department of Economics.
  9. Susanne Schennach, 2012. "Measurement error in nonlinear models- a review," CeMMAP working papers CWP41/12, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
  10. Shiu, Ji-Liang, 2014. "An alternative identification of nonlinear dynamic panel data models with unobserved covariates," Economics Letters, Elsevier, vol. 122(2), pages 338-342.

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