Practical Data Analysis Cookbook

Chapter 10. Discrete Choice Models

In this chapter, you will learn the following recipes:

• Preparing a dataset to estimate discrete choice models
• Estimating the well-known Multinomial Logit model
• Testing for violations of the Independence from Irrelevant Alternatives
• Handling IIA violations with the Nested Logit model
• Managing sophisticated substitution patterns with the Mixed Logit model

Introduction

Discrete choice models (DCMs) aim at predicting which alternative a person will choose. The models share similarities with logistic regression although with some fundamental differences in assumptions about the distribution of error terms.

The theory of DCMs has its roots in the random utility theory and assumption that a rational person will always choose ...