Nonparametric Identification of Dynamic Models with Unobserved State Variables

Yingyao Hu and Matthew Shum ()

Economics Working Paper Archive from The Johns Hopkins University,Department of Economics

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.

Date: 2008-04
New Economics Papers: this item is included in nep-dcm and nep-ecm
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Citations: View citations in EconPapers (28)

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Related works:
Journal Article: Nonparametric identification of dynamic models with unobserved state variables (2012)
Working Paper: Nonparametric identification of dynamic models with unobserved state variables (2008)
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Persistent link: https://EconPapers.repec.org/RePEc:jhu:papers:543

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