Smoothed Quantile Regression for Panel Data
Smoothed Quantile Regression for Panel Data
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DOI：
10.2139/ssrn.2606188
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发表时间：
2015-05
期刊：
影响因子：
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通讯作者：
A. Galvao;Kengo Kato
中科院分区：
文献类型：
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作者：
A. Galvao;Kengo Kato
本文研究了面板数据的分位数回归模型的固定效应估计。在个体数和周期以相同速率增长的渐近框架下，我们证明了光滑目标函数的固定效应估计量具有一个均值偏置的极限正态分布，并给出了渐近偏置的解析形式。基于渐近分析得到的解析偏差公式，提出了一种单步偏差校正估计量。重要的是，我们的结果涵盖了观测随时间变化而变化的情况。我们通过仿真说明了偏差校正的效果。
This paper studies fixed effects estimation of quantile regression models for panel data. Under an asymptotic framework where both the numbers of individuals and time periods grow at the same rate, we show that the fixed-effects estimator for the smoothed objective function has a limiting normal distribution with a bias in the mean, and provide the analytic form of the asymptotic bias. We propose a one-step bias correction estimator based on the analytic bias formula obtained from the asymptotic analysis. Importantly, our results cover the case that observations are dependent over time. We illustrate the effects of the bias correction through simulations.