Random effects probit and logit: understanding predictions and marginal effects

2018/02/23 by James R. Bland, Amanda Cook · 1 voice · 1 citation
Economics, Econometrics and Finance · Social Sciences · Decision Sciences · #Economic theories and models #Experimental Behavioral Economics Studies #Decision-Making and Behavioral Economics

paper · doi:10.1080/13504851.2018.1441498

openalex publication_date 2018/02/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/02

Abstract

Random effects probit and logit are nonlinear models, so we need predicted probabilities and marginal effects to communicate the economic significance of results. In these calculations, how one treats the individual-specific error term matters. Should one (i) set them equal to zero or (ii) integrate them out? We argue that (ii) is the quantity that most readers would expect to see. We discuss these in the context of the statistical package Stata, which changed its default predictions from (i) to (ii) in version 14. In Appendix 5, we illustrate how to calculate predictions and marginal effects using method (ii) in Stata 13 and earlier.

Cited by

• Clinic and care: associations with adolescent antiretroviral therapy adherence in a prospective cohort in South Africa

Discussions

• doi.org/10.1080/1350... [bsky, 2 points, 0 comments]

Related

• probitfe and logitfe: Bias corrections for probit and logit models with two-way fixed effects
• Log Odds and the Interpretation of Logit Models
• Total, Direct, and Indirect Effects in Logit and Probit Models
• Interpreting and Understanding Logits, Probits, and Other Nonlinear Probability Models
• Predicted Probabilities and Inference with Multinomial Logit