Modeling methods for discrete choice analysis

被引：7

作者：

Ben-Akiva M. ^{[1
]}

Mcfadden D. ^{[2
]}

Abe M. ^{[3
]}

Böckenholt U. ^{[4
]}

Bolduc D. ^{[5
]}

Gopinath D. ^{[6
]}

Morikawa T. ^{[7
]}

Ramaswamy V. ^{[8
]}

Rao V. ^{[9
]}

Revelt D. ^{[10
]}

Steinberg D. ^{[11
]}

机构：

[1] Massachusetts Inst. of Technology, Dept. of Civ. and Environ. Eng., Cambridge, MA 02139

[2] University of California, Department of Economics, 655 Evans Hall, Berkeley

[3] University of Illinois at Chicago, Department of Marketing, MC 243, Chicago, IL 60607

[4] Univ. Illinois at Urbana-Champaign, Department of Psychology, Champaign, IL 61820

[5] Université Laval, Dept. d'Économique, Pavillon J.-A. de Sève, Sainte-Foy

[6] Mercer Management Consulting, Lexington, MA 02173

[7] Nagoya University, Department of Civil Engineering, Chikusa-ku, Nagoya

[8] NBD Bancorp, Dept. of Business Administration, University of Michigan, Ann Arbor, MI 48109

[9] Johnson Grad. School of Management, Cornell University, 529 Malott Hall, Ithaca

[10] University of California at Berkeley, Department of Economics

[11] San Diego State University, Department of Economics, San Diego

来源：

Marketing Letters | 1997年 / 8卷 / 3期

关键词：

Discrete choice models; Multinomial probit; Sample design; Simulation estimation;

D O I：

10.1023/A:1007956429024

中图分类号：

学科分类号：

摘要：

This paper introduces new forms, sampling and estimation approaches for discrete choice models. The new models include behavioral specifications of latent class choice models, multinomial probit, hybrid logit, and non-parametric methods. Recent contributions also include new specialized choice based sample designs that permit greater efficiency in data collection. Finally, the paper describes recent developments in the use of simulation methods for model estimation. These developments are designed to allow the applications of discrete choice models to a wider variety of discrete choice problems.

引用

页码：273 / 286

页数：13

共 38 条

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