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Working Papers, Department of Economics, The Johns Hopkins University >
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http://hdl.handle.net/10419/49893
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| Title: | | Nonparametric identification of dynamic models with unobserved state variables  |
| 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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