Cluster robust covariance matrix estimation in panel quantile regression with individual fixed effects

Artikel

This study develops cluster robust inference methods for panel quantile regression (QR) models with individual fixed effects, allowing for temporal correlation within each individual. The conventional QR standard errors can seriously underestimate the uncertainty of estimators and, therefore, overestimate the significance of effects, when outcomes are serially correlated. Thus, we propose a clustered covariance matrix (CCM) estimator to solve this problem. The CCM estimator is an extension of the heteroskedasticity and autocorrelation consistent covariance matrix estimator for QR models with fixed effects. The autocovariance element in the CCM estimator can be substantially biased, due to the incidental parameter problem. Thus, we develop a bias-correction method for the CCM estimator. We derive an optimal bandwidth formula that minimizes the asymptotic mean squared errors, and propose a data-driven bandwidth selection rule. We also propose two cluster robust tests, and establish their asymptotic properties. We then illustrate the practical usefulness of the proposed methods using an empirical application.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Journal: Quantitative Economics ; ISSN: 1759-7331 ; Volume: 11 ; Year: 2020 ; Issue: 2 ; Pages: 579-608 ; New Haven, CT: The Econometric Society

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions ; Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
• Thema: Cluster robust standard errors ; quantile regression ; panel data ; heteroskedasticity and autocorrelation consistent covariance matrix estimation

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Yoon, Jungmo ; Galvão Júnior, Antônio Fialho
• Ereignis: Veröffentlichung
• (wer): The Econometric Society
• (wo): New Haven, CT
• (wann): 2020

Weitere Informationen

• DOI: doi:10.3982/QE802
• Handle: http://hdl.handle.net/10419/217196
• Letzte Aktualisierung: 10.03.2025, 11:45 MEZ