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Title:Nonparametric identification of dynamic models with unobserved state variables PDF Logo
Authors:Hu, Yingyao
Shum, Matthew
Issue Date:2008
Series/Report no.:Working papers // the Johns Hopkins University, Department of Economics 543
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.
Document Type:Working Paper
Appears in Collections:Working Papers, Department of Economics, The Johns Hopkins University

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