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dc.contributor.authorHu, Yingyaoen_US
dc.contributor.authorShum, Matthewen_US
dc.date.accessioned2010-03-19en_US
dc.date.accessioned2011-09-27T15:21:24Z-
dc.date.available2011-09-27T15:21:24Z-
dc.date.issued2008en_US
dc.identifier.urihttp://hdl.handle.net/10419/49893-
dc.description.abstractWe 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_US
dc.language.isoengen_US
dc.publisherJohns Hopkins Univ., Dep. of Economics Baltimore, Md.en_US
dc.relation.ispartofseriesWorking papers // the Johns Hopkins University, Department of Economics 543en_US
dc.subject.ddc330en_US
dc.subject.stwMarkovscher Prozessen_US
dc.subject.stw├ľkonometrieen_US
dc.titleNonparametric identification of dynamic models with unobserved state variablesen_US
dc.typeWorking Paperen_US
dc.identifier.ppn573519633en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
Appears in Collections:Working Papers, Department of Economics, The Johns Hopkins University

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