A New Estimation Approach to Integrate Latent Psychological Constructs in Choice Modeling

In the current paper, the authors propose a new multinomial probit-based model formulation for integrated choice and latent variable (ICLV) models, which, as the authors show in the paper, has several important advantages relative to the traditional logit kernel-based ICLV formulation. Combining this multivariate probit (MNP)-based ICLV model formulation with Bhat’s maximum approximate composite marginal likelihood (MACML) inference approach resolves the specification and estimation challenges that are typically encountered with the traditional ICLV formulation estimated using simulation approaches. The proposed approach can provide very substantial computational time advantages, because the dimensionality of integration in the log-likelihood function is independent of the number of latent variables. Further, the proposed approach easily accommodates ordinal indicators for the latent variables, as well as combinations of ordinal and continuous response indicators. The approach can be extended in a relatively straightforward fashion to also include nominal indicator variables. A simulation exercise in the virtual context of travel mode choice shows that the MACML inference approach is very effective at recovering parameters. The time for convergence is of the order of 30 minutes to 80 minutes for sample sizes ranging from 500 observations to 2000 observations, in contrast to much longer times for convergence experienced in typical ICLV model estimations.

• Record URL:
  • http://ctr.utexas.edu/wp-content/uploads/DSTOP102.pdf
• 
• Record URL:
  • https://rosap.ntl.bts.gov/view/dot/38996
• Supplemental Notes:
  • This document was sponsored by the U.S. Department of Transportation, University Transportation Centers Program.
• Corporate Authors:

  Data-Supported Transportation Operations and Planning Center

  University of Texas at Austin
  Austin, TX  United States  78701

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Authors:
  • Bhat, Chandra R
  • 0000-0002-0715-8121
  • Dubey, Subodh K
• Publication Date: 2014-9

Language

• English

Media Info

• Media Type: Digital/other
• Features: References; Tables;
• Pagination: 43p

Subject/Index Terms

• TRT Terms: Choice models; Estimation theory; Mode choice; Multinomial probits; Psychological aspects; Simulation
• Subject Areas: Planning and Forecasting; Transportation (General);

Filing Info

• Accession Number: 01626800
• Record Type: Publication
• Report/Paper Numbers: D-STOP/2016/102
• Contract Numbers: DTRT13-G-UTC58
• Files: UTC, NTL, TRIS, ATRI, USDOT
• Created Date: Feb 27 2017 9:26AM