Interpreting Discrete Choice Models

Garrett Glasgow

doi:10.1017/9781108873000

Series: Elements in Quantitative and Computational Methods for the Social Sciences

Interpreting Discrete Choice Models

Published online by Cambridge University Press: 21 April 2022

Garrett Glasgow

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Garrett Glasgow: Affiliation:
NERA Economic Consulting, New York

Summary

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.

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discrete choice first difference marginal effect elasticity odds ratio

Type: Element
Information: Series: Elements in Quantitative and Computational Methods for the Social Sciences

DOI: https://doi.org/10.1017/9781108873000 [Opens in a new window]

Online ISBN: 9781108873000

Publisher: Cambridge University Press

Print publication: 12 May 2022

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