A new mixed MNP model accommodating a variety of dependent non-normal coefficient distributions
Chandra R Bhat, Patricia S Lavieri
THEORY AND DECISION | SPRINGER | Published : 2018
In this paper, we propose a general copula approach to accommodate non-normal continuous mixing distributions in multinomial probit models. In particular, we specify a multivariate mixing distribution that allows different marginal continuous parametric distributions for different coefficients. A new hybrid estimation technique is proposed to estimate the model, which combines the advantageous features of each of the maximum simulated likelihood inference technique and Bhat’s maximum approximate composite marginal likelihood inference approach. The effectiveness of our formulation and inference approach is demonstrated through simulation exercises and an empirical application.
This research was partially supported by the U.S. Department of Transportation through the Data-Supported Transportation Operations and Planning (D-STOP) Tier 1 University Transportation Center. The first author would like to acknowledge support from a Humboldt Research Award from the Alexander von Humboldt Foundation, Germany. The authors are grateful to Lisa Macias for her help in formatting this document, and to two anonymous referees who provided useful comments on an earlier version of the paper.