On the unbiased asymptotic normality of quantile regression with fixed effects

Antonio Galvao, Jiaying Gu and Stanislav Volgushev

Journal of Econometrics, 2020, vol. 218, issue 1, 178-215

Abstract: Nonlinear panel data models with fixed individual effects provide an important set of tools for describing microeconometric data. In a large class of such models (including probit, proportional hazard and quantile regression to name just a few) it is impossible to difference out the individual effects, and inference is usually justified in a ‘large n large T’ asymptotic framework. However, there is a considerable gap in the type of assumptions that are currently imposed in models with smooth score functions (such as probit, and proportional hazard) and quantile regression. In the present paper we show that this gap can be bridged and establish unbiased asymptotic normality for fixed effects quantile regression panels under conditions on n,T that are very close to what is typically assumed in standard nonlinear panels. Our results considerably improve upon existing theory and show that quantile regression is applicable to the same type of panel data (in terms of n,T) as other commonly used nonlinear panel data models. Thorough numerical experiments confirm our theoretical findings.

Keywords: Quantile regression; Panel data; Fixed effects; Asymptotics (search for similar items in EconPapers)
JEL-codes: C13 C21 C23 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (21)

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Working Paper: On the Unbiased Asymptotic Normality of Quantile Regression with Fixed Effects (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:218:y:2020:i:1:p:178-215

DOI: 10.1016/j.jeconom.2019.12.017

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