A Monté Carlo Comparison of Estimators for the Multinomial Logit Model
通过蒙特卡洛模拟比较了多项Logit模型的最大似然估计与最小Logit卡方估计等五种方法，发现最大似然估计在点估计、预测精度和统计推断上表现更优，支持营销研究中优先使用最大似然估计。
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.