Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/64796
Full metadata record
DC FieldValueLanguage
dc.contributor.authorHoderlein, Stefanen_US
dc.contributor.authorNesheim, Larsen_US
dc.contributor.authorSimoni, Annaen_US
dc.date.accessioned2012-04-18en_US
dc.date.accessioned2012-10-16T13:08:42Z-
dc.date.available2012-10-16T13:08:42Z-
dc.date.issued2012en_US
dc.identifier.pidoi:10.1920/wp.cem.2012.0912en_US
dc.identifier.urihttp://hdl.handle.net/10419/64796-
dc.description.abstractIn structural economic models, individuals are usually characterized as solving a decision problem that is governed by a finite set of parameters. This paper discusses the nonparametric estimation of the probability density function of these parameters if they are allowed to vary continuously across the population. We establish that the problem of recovering the probability density function of random parameters falls into the class of non-linear inverse problem. This framework helps us to answer the question whether there exist densities that satisfy this relationship. It also allows us to characterize the identified set of such densities. We obtain novel conditions for point identification, and establish that point identification is generically weak. Given this insight, we provide a consistent nonparametric estimator that accounts for this fact, and derive its asymptotic distribution. Our general framework allows us to deal with unobservable nuisance variables, e.g., measurement error, but also covers the case when there are no such nuisance variables. Finally, Monte Carlo experiments for several structural models are provided which illustrate the performance of our estimation procedure.en_US
dc.language.isoengen_US
dc.publisher|aCentre for Microdata Methods and Practice (cemmap) |cLondonen_US
dc.relation.ispartofseries|acemmap working paper |xCWP09/12en_US
dc.subject.ddc330en_US
dc.subject.keywordStructural Modelsen_US
dc.subject.keywordHeterogeneityen_US
dc.subject.keywordNonparametric Identificationen_US
dc.subject.keywordRandom Coefficientsen_US
dc.subject.keywordInverse Problemsen_US
dc.titleSemiparametric estimation of random coefficients in structural economic modelsen_US
dc.typeWorking Paperen_US
dc.identifier.ppn690200544en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US

Files in This Item:
File
Size
1.01 MB





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