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，使得推断方法对广泛存在的共同冲击具有稳健性。此类冲击引起横截面相关性，这在许多经济和金融面板数据中至关重要，但现有FEQR理论通常假设横截面独立且要求$T \gg N$，因而在很大程度上忽略了这种相关性。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，共同冲击从根本上改变了渐近协方差结构，使得传统协方差估计量不一致，并提出了一个在共同冲击存在与否的情况下均保持一致的简单协方差估计量。因此，所提出的方法无需预先了解依赖结构即可提供有效的稳健推断，显著扩展了FEQR方法在实际面板数据环境中的适用性。