Mixed MNL models for discrete response

Journal of Applied EconometricsVolume 15, Issue 5 p. 447-470 Research ArticleFree Access Mixed MNL models for discrete response Daniel McFadden, Corresponding Author Daniel McFadden mcfadden@econ.berkeley.edu Department of Economics, University of California, Berkeley, CA, 94720-3880, USADepartment of Economics, University of California, Berkeley, CA 94720-3880, USASearch for more papers by this authorKenneth Train, Kenneth Train Department of Economics, University of California, Berkeley, CA, 94720-3880, USASearch for more papers by this author Daniel McFadden, Corresponding Author Daniel McFadden mcfadden@econ.berkeley.edu Department of Economics, University of California, Berkeley, CA, 94720-3880, USADepartment of Economics, University of California, Berkeley, CA 94720-3880, USASearch for more papers by this authorKenneth Train, Kenneth Train Department of Economics, University of California, Berkeley, CA, 94720-3880, USASearch for more papers by this author First published: 29 December 2000 https://doi.org/10.1002/1099-1255(200009/10)15:5<447::AID-JAE570>3.0.CO;2-1Citations: 2,002AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract This paper considers mixed, or random coefficients, multinomial logit (MMNL) models for discrete response, and establishes the following results. Under mild regularity conditions, any discrete choice model derived from random utility maximization has choice probabilities that can be approximated as closely as one pleases by a MMNL model. Practical estimation of a parametric mixing family can be carried out by Maximum Simulated Likelihood Estimation or Method of Simulated Moments, and easily computed instruments are provided that make the latter procedure fairly efficient. The adequacy of a mixing specification can be tested simply as an omitted variable test with appropriately defined artificial variables. An application to a problem of demand for alternative vehicles shows that MMNL provides a flexible and computationally practical approach to discrete response analysis. Copyright © 2000 John Wiley & Sons, Ltd. Citing Literature Volume15, Issue5September/October 2000Pages 447-470 ReferencesRelatedInformation