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Estimation and Inference for the $τ$-Quantile of Individual Heterogeneous Coefficient

Antonio F. Galvao, Ulrich Hounyo, Jiahao Lin

arXiv 3 May 2026 · Econometrics

arXiv:2605.01923 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper proposes estimation and inference procedures for the quantiles of individual heterogeneous slope coefficients within panel data. We develop a two-step quantile estimation framework for analyzing heterogeneity in individual coefficients. Unlike conventional panel quantile regression, which focuses on outcome heterogeneity, our approach targets the $τ$-quantile of the cross-sectional distribution of individual-specific slopes. We establish asymptotic theory under both stochastic and deterministic designs, with convergence rates $\sqrt{N}$ and $\sqrt{N\sqrt{T}}$, respectively. We also develop two corresponding bootstrap procedures for practical inference, and formally establish their validity. The suggested methods are of practical interest since they require weaker sample size growth conditions than standard fixed-effect quantile regression, and accommodate large $N$ settings. Numerical simulations and an application to mutual fund performance illustrate the proposed methods and the heterogeneity patterns they reveal across quantiles.

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Galvao, Antonio F and Wang, Liang (2015) Efficient minimum distance estimator for quantile regression fixed effects panel data self1.00053100%
2Galvao, Antonio F and Kato, Kengo (2016) Smoothed quantile regression for panel data self0.84333100%
3Antonio F. Galvao and Jiaying Gu and Stanislav Volgushev (2020) On the Unbiased Asymptotic Normality of Quantile Regression with Fixed Effects self0.64422100%
4Fernández-Val, Iván and Gao, Wayne Yuan and Liao, Yuan and Vella, Fr… (2025) Dynamic heterogeneous distribution regression panel models, with an application to labor income processes0.64422100%
5Li, Kunpeng and Cui, Guowei and Lu, Lina (2020) Efficient estimation of heterogeneous coefficients in panel data models with common shocks0.64422100%
6Pesaran, M Hashem and Shin, Yongcheol and Smith, Ron P (1999) Pooled mean group estimation of dynamic heterogeneous panels0.64422100%
7Feller, William (1966) An introduction to probability theory and its applications, Volume 20.5112250%
8Carhart, Mark M (1997) On persistence in mutual fund performance0.51121100%
9Fama, Eugene F and French, Kenneth R (1993) Common risk factors in the returns on stocks and bonds0.51121100%
10Kengo Kato and Antonio F. Galvao and Gabriel V. Montes-Rojas (2012) Asymptotics for panel quantile regression models with individual effects0.40511100%

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