This paper develops an asymptotic and inferential theory for fixed-effects panel quantile regression (FEQR) that delivers inference robust to pervasive common shocks. Such shocks induce cross-sectional dependence that is central in many economic and financial panels but largely ignored in existing FEQR theory, which typically assumes cross-sectional independence and requires $T \gg N$. We show that the standard FEQR estimator remains asymptotically normal under the mild condition $(\log N)^2/T \to 0$, thereby accommodating empirically relevant regimes, including those with $T \ll N$. We further show that common shocks fundamentally alter the asymptotic covariance structure, rendering conventional covariance estimators inconsistent, and we propose a simple covariance estimator that remains consistent both in the presence and absence of common shocks. The proposed procedure therefore provides valid robust inference without requiring prior knowledge of the dependence structure, substantially expanding the applicability of FEQR methods in realistic panel data settings.

翻译：本文发展了固定效应面板分位数回归(FEQR)的渐近与推断理论，使其在面对普遍存在的共同冲击时仍能提供稳健推断。此类冲击引起的截面依赖性是许多经济与金融面板数据的核心特征，但现有FEQR理论通常假设截面独立性并需要$T \gg N$，从而在很大程度上忽视了这一特征。我们证明，在温和条件$(\log N)^2/T \to 0$下，标准FEQR估计量仍保持渐近正态性，从而适用于包括$T \ll N$在内的经验相关情形。我们进一步证明，共同冲击会根本性地改变渐近协方差结构，导致传统协方差估计量不一致，并提出一种在存在或不存在共同冲击时均保持一致的简单协方差估计量。因此，所提出的方法无需事先了解依赖结构即可提供有效的稳健推断，大幅扩展了FEQR方法在现实面板数据环境中的适用性。