This paper concentrates on the comparison of different discrete choice models using data concerning purchases of different brands of crackers in USA. Five logit models are proposed: multinomial logit (MNL), mixed logit (ML), multinomial logit which accounts for heterogeneity between the customers (MNLH), multinomial logit extended with a brand loyalty variable (MNLB), and multinomial logit with a combination of the two extensions to MNL mentioned above (MNLC). Using the Cracker data set from the Ecdat package in the R software, it was found that ML outperforms all the other models, and that the extensions applied to MNL help in improving MNL. Therefore, it was concluded that using a mixed logit model is the best way to model an unordered categorical dependent variable.

Additional Metadata
Thesis Advisor Vester, J.S.
Persistent URL hdl.handle.net/2105/50097
Series Econometrie
Citation
Pfaff, L.J.H. (2019, August 13). Comparison of Multinomial Logit and Mixed Logit. Econometrie. Retrieved from http://hdl.handle.net/2105/50097