Performance Analysis of Mixed Logit Models for Discrete Choice Models

Abstract

Mixed Logit model  (MXL) is generated from Multinomial Logit model (MNL) for discrete, i.e. nominal, data. It eliminates its limitations particularly on estimating the correlation among responses.  In the MNL, the probability equations are presented in the closed form and it is contrary with in the MXL. Consequently, the calculation of the probability value of each alternative get simpler in the MNL, meanwhile it needs the numerical methods for estimation in the MXL.  In this study, we investigated the performance of maximum likelihood estimation (MLE) in the MXL and MNL into two cases, the low and high correlation circumstances among responses. The performance is measured based on differencing actual and estimation value.  The simulation study and real cases show that the MXL model is more accurate than the MNL model. This model can estimates the correlation among response as well. The study concludes that the MXL model is suggested to be used if there is a high correlation among responses.