Estimation and Inference in Quantile Regressions with Multiple Fixed Effects

Fernando Rios-Avila, Andrey Ramos, Gustavo Canavire-Bacarreza and Leonardo Siles
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Fernando Rios-Avila: Universidad Privada Boliviana
Andrey Ramos: Bank of Spain
Leonardo Siles: Universidad de Chile

International Center for Public Policy Working Paper Series, at AYSPS, GSU from International Center for Public Policy, Andrew Young School of Policy Studies, Georgia State University

Abstract: This paper proposes a method to estimate quantile regression models with multiple fixed effects. We extend the quantile–via–moments estimator of Machado and Santos Silva (2019) and suggest a computationally efficient Frisch–Waugh–Lovell residualization to partial out additive fixed effects in both the location and scale equations. A unified influence-function inference framework is derived, accommodating heteroskedasticity-robust, clustered, and feasible GLS standard errors. Monte Carlo simulations provide strong support for the validity of the proposed procedure in applications with multi-way unobserved heterogeneity and intra-cluster correlated disturbances. An empirical application to Climate Growth-at-Risk illustrates how temperature shocks affect the conditional distribution of macroeconomic outcomes in a panel of 194 countries. Our findings suggest that in low income countries, downside risks to growth are more strongly linked to temperature shocks than the central tendency or upside risks.

Pages: 33 pages
Date: 2026-03
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Persistent link: https://EconPapers.repec.org/RePEc:ays:ispwps:paper2615

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