HAC Covariance Matrix Estimation in Quantile Regression

Antonio F. Galvao, Jungmo Yoon
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Abstract

This study considers an estimator for the asymptotic variance-covariance matrix in time-series quantile regression models which is robust to the presence of heteroskedasticity and autocorrelation. When regression errors are serially correlated, the conventional quantile regression standard errors are invalid. The proposed solution is a quantile analogue of the Newey-West robust standard errors. We establish the asymptotic properties of the heteroskedasticity and autocorrelation consistent (HAC) covariance matrix estimator and provide an optimal bandwidth selection rule. The quantile sample autocorrelation coefficient is biased toward zero in finite sample which adversely affects the optimal bandwidth estimation. We propose a simple alternative estimator that effectively reduces the finite sample bias. Numerical simulations provide evidence that the proposed HAC covariance matrix estimator significantly improves the size distortion problem. We examine the impacts of the expansion of renewable energy resources on electricity prices to illustrate the usefulness of the proposed robust standard error.
分位数回归中的HAC协方差矩阵估计
本文研究了时间序列分位数回归模型中渐近方差-协方差矩阵的估计量,该估计量对异方差和自相关的存在具有鲁棒性。当回归误差呈序列相关时,传统的分位数回归标准误差无效。所提出的解决方案是纽西鲁棒标准误差的分位数模拟。我们建立了异方差和自相关一致(HAC)协方差矩阵估计的渐近性质,并给出了最优带宽选择规则。在有限样本中,分位数样本自相关系数偏向于零,这对最优带宽估计产生不利影响。我们提出了一个简单的替代估计器,有效地减少了有限样本偏差。数值模拟结果表明,所提出的HAC协方差矩阵估计方法能显著改善尺寸失真问题。我们考察了可再生能源的扩张对电价的影响,以说明所提出的稳健标准误差的有用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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