Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/22042 
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dc.contributor.authorLiesenfeld, Romanen
dc.contributor.authorRichard, Jean-Françoisen
dc.date.accessioned2009-01-29T14:13:43Z-
dc.date.available2009-01-29T14:13:43Z-
dc.date.issued2007-
dc.identifier.urihttp://hdl.handle.net/10419/22042-
dc.description.abstractIn this paper we discuss parameter identification and likelihood evaluation for multinomial multiperiod Probit models. It is shown in particular that the standard autoregressive specification used in the literature can be interpreted as a latent common factor model. However, this specification is not invariant with respect to the selection of the baseline category. Hence, we propose an alternative specification which is invariant with respect to such a selection and identifies coefficients characterizing the stationary covariance matrix which are not identified in the standard approach. For likelihood evaluation requiring high-dimensional truncated integration we propose to use a generic procedure known as Efficient Importance Sampling (EIS). A special case of our proposed EIS algorithm is the standard GHK probability simulator. To illustrate the relative performance of both procedures we perform a set Monte-Carlo experiments. Our results indicate substantial numerical e?ciency gains of the ML estimates based on GHK-EIS relative to ML estimates obtained by using GHK.en
dc.language.isoengen
dc.publisher|aKiel University, Department of Economics |cKielen
dc.relation.ispartofseries|aEconomics Working Paper |x2007-26en
dc.subject.jelC35en
dc.subject.jelC15en
dc.subject.ddc330en
dc.subject.keywordDiscrete choiceen
dc.subject.keywordImportance samplingen
dc.subject.keywordMonte-Carlo integrationen
dc.subject.keywordPanel dataen
dc.subject.keywordParameter identificationen
dc.subject.keywordSimulated maximum likelihooden
dc.subject.stwProbit-Modellen
dc.subject.stwSchätztheorieen
dc.subject.stwMaximum-Likelihood-Methodeen
dc.subject.stwTheorieen
dc.titleThe Multinomial Multiperiod Probit Model: Identification and Efficient Estimation-
dc.typeWorking Paperen
dc.identifier.ppn546256643en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:zbw:cauewp:6340en

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