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Modeling Methods for Discrete Choice Analysis
Authors:Ben-Akiva  Moshe  Mcfadden  Daniel  Abe  Makoto  Böckenholt  Ulf  Bolduc  Denis  Gopinath  Dinesh  Morikawa  Takayuki  Ramaswamy  Venkatram  Rao  Vithala  Revelt  David  Steinberg  Dan
Institution:(1) Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA, 02139;(2) Department of Economics, University of California, 655 Evans Hall, Berkeley, CA, 94720;(3) Department of Marketing, University of Illinois at Chicago, 601 S. Morgan Street, MC 243, Chicago, IL, 60607;(4) Department of Psychology, University of Illinois at Urbana-Champaign, 603 East Daniel Street, Champaign, IL, 61820;(5) Département drsquoéconomique, Université Laval, Pavillon J.-A. De Sève, Sainte-Foy, Quebec, G1K 7P4, Canada;(6) Mercer Management Consulting, 33 Hayden Avenue, Lexington, MA, 02173;(7) Department of Civil Engineering, Nagoya University, Chikusa-ku, Nagoya, 464-01, Japan;(8) NBD Bancorp, University of Michigan, 701 Tappan Street, Ann Arbor, MI, 48109;(9) Johnson Graduate School of Management, Cornell University, 529 Malott Hall, Ithaca, NY, 14853;(10) Department of Economics, University of California at Berkeley, USA;(11) Department of Economics, San Diego State University, San Diego, CA, 92182
Abstract:This paper introduces new forms, sampling and estimation approaches fordiscrete choice models. The new models include behavioral specifications oflatent class choice models, multinomial probit, hybrid logit, andnon-parametric methods. Recent contributions also include new specializedchoice based sample designs that permit greater efficiency in datacollection. Finally, the paper describes recent developments in the use ofsimulation methods for model estimation. These developments are designed toallow the applications of discrete choice models to a wider variety ofdiscrete choice problems.
Keywords:discrete choice models  multinomial probit  simulation estimation  sample design
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