A new estimation approach for the multiple discrete–continuous probit (MDCP) choice model |
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| Institution: | 1. The University of Texas at Austin, Department of Civil, Architectural and Environmental Engineering, 301 E. Dean Keeton St., Stop C1761, Austin, TX 78712-1172, United States;2. King Abdulaziz University, Jeddah 21589, Saudi Arabia;1. Institute of Applied Remote Sensing and Information Technology, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, Zhejiang, China;2. Key Laboratory of Agricultural Remote Sensing and Information System of Zhejiang Province, Hangzhou 310058, Zhejiang, China;3. Center for Forest Resource Monitoring of Zhejiang Province, Hangzhou 310020, Zhejiang, China;1. College of Physics and Materials Science, Henan Normal University, Xinxiang 453007, China;2. School of Environment, Henan Normal University, Xinxiang 453007, China;1. Department of Economics, University of Oxford, UK;2. Survey Research Center, Institute for Social Research, University of Michigan, USA;1. Key Laboratory for Green Chemical Process of Ministry of Education, School of Environmental Ecology and Biological Engineering, Wuhan Institute of Technology, Wuhan, 430205, People’s Republic of China;2. School of Energy Science and Engineering, Central South University, Changsha, 410083, People’s Republic of China;3. School of Minerals Processing and Bioengineering, Central South University, Changsha, 410083, People’s Republic of China;4. School of Resources and Safety Engineering, Wuhan Institute of Technology, Wuhan, 430073, People’s Republic of China;1. Key Laboratory of Transport Industry of Management, Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area (Chang‘an University), Xi’an City, Shannxi Province 710064, PR China;2. School of Highway, Chang’an University, Xi’an City, Shannxi Province 710064, PR China;3. Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;4. School of Transportation & Logistics, Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian City, Liaoning Province 116023, PR China;5. School of Transportation Engineering, Dalian Maritime University, Dalian City, Liaoning Province 116026, PR China |
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| Abstract: | This paper develops a blueprint (complete with matrix notation) to apply Bhat’s (2011) Maximum Approximate Composite Marginal Likelihood (MACML) inference approach for the estimation of cross-sectional as well as panel multiple discrete–continuous probit (MDCP) models. A simulation exercise is undertaken to evaluate the ability of the proposed approach to recover parameters from a cross-sectional MDCP model. The results show that the MACML approach does very well in recovering parameters, as well as appears to accurately capture the curvature of the Hessian of the log-likelihood function. The paper also demonstrates the application of the proposed approach through a study of individuals’ recreational (i.e., long distance leisure) choice among alternative destination locations and the number of trips to each recreational destination location, using data drawn from the 2004 to 2005 Michigan statewide household travel survey. |
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| Keywords: | Multiple discrete–continuous model Maximum approximate composite marginal likelihood Recreation choice |
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