A systematic comparison of continuous and discrete mixture models

Abstract

Modellers are increasingly relying on the use of continuous random coefficients models, Such as Mixed Logit, for the representation of variations in tastes across individuals. Logit, for the representation of variations in tastes across individuals. In this paper, we provide an in- In this paper, We provide an in- depth comparison of the performance of the Mixed Logit model with that of its far less commonly used depth comparison of the performance of the Mixed Logit Model With That of ITS Commonly used to make less discrete mixture counterpart, making use of a combination of real and simulated datasets. discrete mixture counterpart, making use of a combination of real and simulated datasets. The results not The results not only show significant computational advantages for the discrete mixture approach, but also highlight only show significant computational advantages for the discrete mixture approach, but Also highlights greater flexibility, and show that, across a host of scenarios, the discrete mixture models are able to offer Greater Flexibility, and show That, across a host of scenarios, the discrete mixture models are Able to offer comparable or indeed superior model performance. indeed comparable or superior model performance.

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