A New Approach to Specify and Estimate Non-Normally Mixed Multinomial Probit Models

dc.creatorBhat, Chandra R.en
dc.creatorSidharthan, Raghuprasaden
dc.date.accessioned2013-08-23T15:16:03Zen
dc.date.available2013-08-23T15:16:03Zen
dc.date.issued2012en
dc.description.abstractThe current paper proposes the use of the multivariate skew-normal distribution function to accommodate non-normal mixing in cross-sectional and panel multinomial probit (MNP) models. The combination of skew-normal mixing and the MNP kernel lends itself nicely to estimation using Bhat’s (2011) maximum approximate composite marginal likelihood (MACML) approach. Simulation results for the cross-sectional case show that our proposed approach does well in recovering the underlying parameters, and also highlights the pitfalls of ignoring non-normality of the continuous mixing distribution when such non-normality is present. At the same time, the proposed model obviates the need to assume a pre-specified parametric distribution for the mixing, and allows the estimation of a very flexible, but still parsimonious, mixing distribution form.en
dc.description.departmentCivil, Architectural, and Environmental Engineeringen
dc.identifier.citationBhat, C.R., and R. Sidharthan (2012), "A New Approach to Specify and Estimate Non-Normally Mixed Multinomial Probit Models,"Transportation Research Part B, Vol. 46, No. 7, pp. 817-833.en
dc.identifier.issn0191-2615en
dc.identifier.urihttp://hdl.handle.net/2152/21110en
dc.language.isoengen
dc.publisherElsevieren
dc.source.urihttp://www.journals.elsevier.com/transportation-research-part-b-methodological/en
dc.subjectmultinomial probiten
dc.subjectmixed modelsen
dc.subjectmaximum approximate composite marginal likelihooden
dc.subjectmaximum simulated likelihooden
dc.subjectmultivariate skew-normal distributionen
dc.titleA New Approach to Specify and Estimate Non-Normally Mixed Multinomial Probit Modelsen
dc.typeArticleen

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