Arbeitspapier
Nonparametric identification of dynamic models with unobserved state variables
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.
- Language
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Englisch
- Bibliographic citation
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Series: Working Paper ; No. 543
- Classification
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Wirtschaft
- Subject
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Markovscher Prozess
Ökonometrie
- Event
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Geistige Schöpfung
- (who)
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Hu, Yingyao
Shum, Matthew
- Event
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Veröffentlichung
- (who)
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The Johns Hopkins University, Department of Economics
- (where)
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Baltimore, MD
- (when)
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2008
- Handle
- Last update
-
10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Hu, Yingyao
- Shum, Matthew
- The Johns Hopkins University, Department of Economics
Time of origin
- 2008
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