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Nonparametric identification of dynamic models with unobserved state variables

  • Hu, Yingyao
  • Shum, Matthew

We consider the identification of a Markov process {Wt,Xt∗} when only {Wt} is observed. In structural dynamic models, Wt includes the choice variables and observed state variables of an optimizing agent, while Xt∗ denotes time-varying serially correlated unobserved state variables (or agent-specific unobserved heterogeneity). In the non-stationary case, we show that the Markov law of motion fWt,Xt∗∣Wt−1,Xt−1∗ is identified from five periods of data Wt+1,Wt,Wt−1,Wt−2,Wt−3. In the stationary case, only four observations Wt+1,Wt,Wt−1,Wt−2 are required. Identification of fWt,Xt∗∣Wt−1,Xt−1∗ is a crucial input in methodologies for estimating Markovian dynamic models based on the “conditional-choice-probability (CCP)” approach pioneered by Hotz and Miller.

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Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 171 (2012)
Issue (Month): 1 ()
Pages: 32-44

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Handle: RePEc:eee:econom:v:171:y:2012:i:1:p:32-44
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