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Robit regression in Stata

Author

Listed:
  • Roger B. Newson

    (King’s College London)

  • Milena Falcaro

    (King’s College London)

Abstract

Logistic and probit models are the most popular regression models for binary outcomes. A simple robust alternative is the robit model, which replaces the underlying normal distribution in the probit model with a Student’s t distribution. The heavier tails of the t distribution (compared with the normal distribution) mean that model outliers are less influential. Robit regression models can be fit as generalized linear models with the link function defined as the inverse cumulative t distribution function with a specified number of degrees of freedom; they have been advocated as being particularly suitable for estimating inverse-probability weights and propensity scoring more generally. Here we describe a new command, robit, that implements robit regression in Stata.

Suggested Citation

  • Roger B. Newson & Milena Falcaro, 2023. "Robit regression in Stata," Stata Journal, StataCorp LP, vol. 23(3), pages 658-682, September.
    • Handle: RePEc:tsj:stataj:v:23:y:2023:i:3:p:658-682
    DOI: 10.1177/1536867X231195288
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    Cited by:

    1. Wagner, Joachim, 2023. "Exports and firm survival in times of COVID-19: Evidence from eight European countries," KCG Working Papers 29, Kiel Centre for Globalization (KCG).
    2. Pyka, Vinzenz & Schnabel, Claus, 2023. "Unionization of retired workers in Europe," Discussion Papers 128, Friedrich-Alexander University Erlangen-Nuremberg, Chair of Labour and Regional Economics.
    3. Joachim Wagner, 2023. "Exports and firm survival in times of COVID-19 – Evidence from eight European countries," Working Paper Series in Economics 422, University of Lüneburg, Institute of Economics.

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