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Quantile regressions with multiple fixed effects

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  • Fernando Rios-Avila

    (Levy Economics Institute)

Abstract

Quantile regression (QR) is an estimation strategy that provides richer characterizations of the relationships between dependent and independent variables. Some developments in the literature have focused on extending quantile regression analysis to include individual fixed effects in the framework of panel data, avoiding the incidental parameter problem, under different assumptions. One recent article by Machado-Santos-Silva (2019) proposed a location-scale estimator that allows for the inclusion of individual fixed effects in the framework of panel data, which permits individual effects to vary across quantiles. In this presentation, I propose an extension to this estimator that permits using any number of fixed effects, providing alternative estimators for SE beyond those suggested in Machado-Santos-Silva (2019). I also present the command mmqreg, which implements these extensions.

Suggested Citation

  • Fernando Rios-Avila, 2023. "Quantile regressions with multiple fixed effects," 2023 Stata Conference 01, Stata Users Group.
    • Handle: RePEc:boc:usug23:01
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