Introduction

The choice modeler has available a number of econometric models. Traditionally, the more common models applied to choice data are the multinomial logit (MNL) and nested logit (NL) models. Increasingly, however, choice modelers are estimating the mixed logit (ML) or random parameters logit model. In Chapter 4, we outlined the theory behind this class of models. In this chapter we estimate a range of ML models using Nlogit, including recent developments in scaled mixed logit (or generalized mixed logit). As with Chapters 11 and 13 (MNL model) and Chapter 14 (NL model), we explain in detail the commands necessary to estimate ML models as well as the interpretation of the output generated by Nlogit. An understanding of the theory behind the ML model is presented in Chapter 4; however we anticipate that in reading this chapter you will have a better understanding of the model, at least from an empirical standpoint.

The mixed logit model basic commands

The ML model syntax commands build on the commands of the MNL model discussed in Chapter 11. We begin with the basic ML syntax command, building upon this in later sections as we add to the complexity of the ML model.