المستودع الأكاديمي جامعة المدينة

Estimation of Multinomial Logit Models with Unobserved Heterogeneity Using Maximum Simulated Likelihood

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dc.creator Haan, Peter
dc.creator Uhlendorff, Arne
dc.date 2006
dc.date.accessioned 2013-10-16T07:00:06Z
dc.date.available 2013-10-16T07:00:06Z
dc.date.issued 2013-10-16
dc.identifier http://hdl.handle.net/10419/18466
dc.identifier ppn:510325017
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/10419/18466
dc.description In this paper we suggest a Stata routine for multinomial logit models with unobserved heterogeneity using maximum simulated likelihood based on Halton sequences. The purpose of this paper is twofold: First, we provide a description of the technical implementation of the estimation routine and discuss its properties. Further, we compare our estimation routine to the Stata program gllamm which solves integration using Gauss Hermite quadrature or Bayesian adaptive quadrature. For the analysis we draw on multilevel data about schooling. Our empirical findings show that the estimation techniques lead to approximately the same estimation results. The advantage of simulation over Gauss Hermite quadrature is a marked reduction in computational time for integrals with higher dimensions. Bayesian quadrature, however, leads to very stable results with only a few quadrature points, thus the computational advantage of Halton based simulation vanishes in our example with one and two dimensional integrals.
dc.language eng
dc.publisher Deutsches Institut für Wirtschaftsforschung (DIW) Berlin
dc.relation DIW-Diskussionspapiere 573
dc.rights http://www.econstor.eu/dspace/Nutzungsbedingungen
dc.subject ddc:330
dc.subject multinomial logit model
dc.subject panel data
dc.subject unobserved heterogeneity
dc.subject maximum simulated likelihood
dc.subject Halton sequences
dc.title Estimation of Multinomial Logit Models with Unobserved Heterogeneity Using Maximum Simulated Likelihood
dc.type doc-type:workingPaper


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