Interpreting logit models

Luca J. Uberti ()
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Luca J. Uberti: University of Luxembourg

Stata Journal, 2022, vol. 22, issue 1, 60-76

Abstract: The parameters of logit models are typically difficult to interpret, and the applied literature is replete with interpretive and computational mistakes. In this article, I review a menu of options to interpret the results of logistic regressions correctly and effectively using Stata. I consider marginal effects, partial effects, (contrasts of) predictive margins, elasticities, and odds and risk ratios. I also show that interaction terms are typically easier to interpret in practice than implied by the recent literature on this topic.

Keywords: logit; binary outcome models; nonlinear models; interpretation; interaction terms (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1177/1536867X221083855

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