Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.contributor.authorHu, Yingyaoen_US
dc.contributor.authorShum, Matthewen_US
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.publisher|aJohns Hopkins Univ., Dep. of Economics |cBaltimore, Md.en_US
dc.relation.ispartofseries|aWorking papers // the Johns Hopkins University, Department of Economics |x543en_US
dc.subject.stwMarkovscher Prozessen_US
dc.titleNonparametric identification of dynamic models with unobserved state variablesen_US
dc.typeWorking Paperen_US

Files in This Item:
299.74 kB

Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.