The Composite Marginal Likelihood (CML) Inference Approach with Applications to Discrete and Mixed Dependent Variable Models

The composite marginal likelihood (CML) inference approach is a relatively simple approach that can be used when the full likelihood function is practically infeasible to evaluate due to underlying complex dependencies. The history of the approach may be traced back to the pseudo-likelihood approach of Besag (1974) for modeling spatial data, and has found traction in a variety of fields since, including genetics, spatial statistics, longitudinal analyses, and multivariate modeling. However, the CML method has found little coverage in the econometrics field, especially in discrete choice modeling. This project will fill this gap by identifying the value and potential applications of the method in discrete dependent variable modeling as well as mixed discrete and continuous dependent variable model systems.

Record URL:

Language

English

Project

Status: Completed
Funding: $20000
Contract Numbers:
DTRT13-G-UTC58
Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology

University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590
Project Managers:
Bhat, Chandra
Performing Organizations:
Data-Supported Transportation Operations and Planning Center

University of Texas at Austin
Austin, TX United States 78701
Principal Investigators:
Bhat, Chandra
Start Date: 20130930
Expected Completion Date: 20140930
Actual Completion Date: 20140930
Source Data: 101

Subject/Index Terms

TRT Terms: Choice models; Data analysis; Econometrics; Maximum likelihood method; Spatial analysis
Subject Areas: Economics; Planning and Forecasting; Transportation (General);

Filing Info

Accession Number: 01577891
Record Type: Research project
Source Agency: Data-Supported Transportation Operations and Planning Center
Contract Numbers: DTRT13-G-UTC58
Files: UTC, RiP
Created Date: Oct 6 2015 4:03PM