Abstract

Marketing researchers use the multinomial logit (MNL) model to analyze discrete choice, and estimate parameters either by maximum likelihood (ML) or minimum logit chi square (MLCS). Some controversy persists, however, over which is better. Review articles in marketing recommend ML over MLCS, but the statistics literature suggests that MLCS should be preferred. No studies have directly compared the performance of ML and MLCS in a marketing context. The authors assess the relative performance of ML, MLCS, and three other candidate estimators for MNL marketing applications involving repeated-measures datasets collected by means of multiple-subset designs. In contrast to most previous findings in the statistics literature, the results strongly support the use of ML. ML is found to outperform the other estimators on a variety of point estimation, predictive accuracy, and statistical inference criteria and ML test statistics are found to have asymptotic behavior for datasets involving relatively few replications.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.