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March 14, 2026The Stata Journal Promoting communications on statistics and Stata2 citations

Estimation of quantile regressions with fixed effects

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FRFernando Rios-AvilaARAndrey RamosGCGustavo Canavire-Bacarreza

Key Points

  • The aim is to introduce new commands for fitting quantile regression models with fixed effects, enhancing current methodologies.
  • Introduced qregfe and qregplot commands for quantile regression with fixed effects.
  • Implemented three common panel-data estimators for empirical research.
  • Developed tools for visualizing coefficient changes across quantiles.
  • Provided a unified syntax for fitting various panel-data estimators.
  • Developed visualizations to show how covariate coefficients vary by quantiles.

Abstract

In this article, we introduce two new commands, qregfe and qregplot , that are designed for fitting and visualizing quantile regression models with fixed effects. qregfe provides a unified syntax for implementing three panel-data estimators that are commonly used in empirical research: 1) the correlated random-effects specification of Abrevaya and Dahl (2008, Journal of Business and Economic Statistics 26: 379–397); 2) the two-step location-shift estimator of Canay (2011, Econometrics Journal 14: 368–386); and 3) the method of moments quantile regression approach of Machado and Santos Silva (2019, Journal of Econometrics 213: 145–173). The companion command qregplot produces coefficient–quantile plots, allowing researchers to visualize how the coefficients of each covariate change across the outcome conditional distribution.

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Cite This Study

Rios-Avila et al. (2026) studied this question.

synapsesocial.com/papers/69b4fc1fb39f7826a300cd37https://doi.org/10.1177/1536867x261425793
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