Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/49872
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dc.contributor.authorHu, Yingyaoen_US
dc.contributor.authorShum, Matthewen_US
dc.contributor.authorTan, Weien_US
dc.date.accessioned2011-09-27T15:21:07Z-
dc.date.available2011-09-27T15:21:07Z-
dc.date.issued2010en_US
dc.identifier.urihttp://hdl.handle.net/10419/49872-
dc.description.abstractWe present a method for estimating Markov dynamic models with unobserved state variables which can be serially correlated over time. We focus on the case where all the model variables have discrete support. Our estimator is simple to compute because it is noniterative, and involves only elementary matrix manipulations. Our estimation method is nonparametric, in that no parametric assumptions on the distributions of the unobserved state variables or the laws of motions of the state variables are required. Monte Carlo simulations show that the estimator performs well in practice, and we illustrate its use with a dataset of doctors' prescription of pharmaceutical drugs.en_US
dc.language.isoengen_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 |x558en_US
dc.subject.ddc330en_US
dc.titleA simple estimator for dynamic models with serially correlated unobservablesen_US
dc.typeWorking Paperen_US
dc.identifier.ppn635250888en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US

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