Interpreting Discrete Choice Models

08 June 2022
By Dr. Garett Glasgow

In discrete choice models (models applicable to cases in which an individual makes a choice from a discrete set of choice alternatives), the relationships between the independent variables and the choice probabilities are non-linear, depending on both the value of the independent variable being interpreted and the values of the other independent variables. Thus, interpreting the magnitude of the effects of the independent variables on choice behavior requires the use of additional interpretative techniques.

In Associate Director Dr. Garrett Glasgow’s recent book, Interpreting Discrete Choice Models, published by Cambridge University Press, he describes three basic techniques for calculating the effects of the independent variables on the choice probabilities (first differences, marginal effects and elasticities, and odds ratios), along with methods to account for estimation uncertainty. The interpretation of many well-known discrete choice models (e.g., 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 depth, both theoretically and with detailed examples. The data and computer code necessary to replicate each example are also included.

For more information and to purchase the book, please visit the Cambridge University Press website