Interpreting Discrete Choice Models

Techniques for interpreting commonly used discrete choice models, illustrated with extensive examples including replication code and data.

Garrett Glasgow (Author)

9781108819404, Cambridge University Press

Paperback / softback, published 12 May 2022

75 pages
22.8 x 15.1 x 0.5 cm, 0.132 kg

In discrete choice models the relationships between the independent variables and the choice probabilities are nonlinear, depending on both the value of the particular independent variable being interpreted and the values of the other independent variables. Thus, interpreting the magnitude of the effects (the “substantive effects”) of the independent variables on choice behavior requires the use of additional interpretative techniques. Three common techniques for interpretation are described here: first differences, marginal effects and elasticities, and odds ratios. Concepts related to these techniques are also discussed, as well as methods to account for estimation uncertainty. Interpretation of binary logits, ordered logits, multinomial and conditional logits, and mixed discrete choice models such as mixed multinomial logits and random effects logits for panel data are covered in detail. The techniques discussed here are general, and can be applied to other models with discrete dependent variables which are not specifically described here.

1. Introduction
2. Accounting for Statistical Uncertainty in Estimates of Substantive Effects
3. Substantive Effects in Binary Choice Models
4. Substantive Effects in Ordered Choice Models
5. Substantive Effects in Multinomial Choice Models
6. Interpretation of Mixed Discrete Choice Models
7. Extensions.

Subject Areas: Data mining [UNF], Probability & statistics [PBT], Politics & government [JP], Social research & statistics [JHBC], Society & social sciences [J], Research methods: general [GPS]