A new approach to specify and estimate non-normally mixed multinomial probit models

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• 作者：Chandra R. Bhat ; ^{bhat@mail.utexas.edu} ; Raghuprasad Sidharthan¹ ; ^{raghu@mail.utexas.edu}
• 关键词：Multinomial probit ; Mixed models ; Maximum approximate composite marginal likelihood ; Maximum simulated likelihood ; Multivariate skew-normal distribution
• 刊名：Transportation Research Part B ; Methodological
• 出版年：2012
• 期刊代码：176_01912615
• 类别：et
• 出版时间：August, 2012
• 卷：46
• 期：7
• 页码：817-833
• 文件大小：397 K

摘要

The 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 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.