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https://hdl.handle.net/10419/49893
Kompletter Metadatensatz
DublinCore-Feld | Wert | Sprache |
---|---|---|
dc.contributor.author | Hu, Yingyao | en |
dc.contributor.author | Shum, Matthew | en |
dc.date.accessioned | 2010-03-19 | - |
dc.date.accessioned | 2011-09-27T15:21:24Z | - |
dc.date.available | 2011-09-27T15:21:24Z | - |
dc.date.issued | 2008 | - |
dc.identifier.uri | http://hdl.handle.net/10419/49893 | - |
dc.description.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. | en |
dc.language.iso | eng | en |
dc.publisher | |aThe Johns Hopkins University, Department of Economics |cBaltimore, MD | en |
dc.relation.ispartofseries | |aWorking Paper |x543 | en |
dc.subject.ddc | 330 | en |
dc.subject.stw | Markovscher Prozess | en |
dc.subject.stw | Ökonometrie | en |
dc.title | Nonparametric identification of dynamic models with unobserved state variables | - |
dc.type | Working Paper | en |
dc.identifier.ppn | 573519633 | en |
dc.rights | http://www.econstor.eu/dspace/Nutzungsbedingungen | en |
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