摘要翻译：
Canay(2011)提出的分位数面板数据模型的两步估计器，由于直观简单，计算成本低，近年来在实证研究中得到了广泛的应用。本文回顾了Canay(2011)的估计量，指出在他的渐近分析中，由于对固定效应的估计而导致的估计量偏差被错误地省略了，这种省略将导致对系数的无效推断。为了解决这个问题，我们提出了一种类似的基于平滑分位数回归的易于实现的估计器。建立了新估计量的渐近分布并导出了其渐近偏差的解析表达式。基于这些结果，我们展示了如何在解析和分裂面板折刀偏差修正的基础上进行渐近有效的推断。最后，利用有限样本模拟来支持我们的理论分析，并说明面板数据分位数回归中偏差校正的重要性。
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英文标题：
《A Simple Estimator for Quantile Panel Data Models Using Smoothed
  Quantile Regressions》
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作者：
Liang Chen and Yulong Huo
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最新提交年份：
2019
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  Canay (2011)'s two-step estimator of quantile panel data models, due to its simple intuition and low computational cost, has been widely used in empirical studies in recent years. In this paper, we revisit the estimator of Canay (2011) and point out that in his asymptotic analysis the bias of his estimator due to the estimation of the fixed effects is mistakenly omitted, and that such omission will lead to invalid inference on the coefficients. To solve this problem, we propose a similar easy-to-implement estimator based on smoothed quantile regressions. The asymptotic distribution of the new estimator is established and the analytical expression of its asymptotic bias is derived. Based on these results, we show how to make asymptotically valid inference based on both analytical and split-panel jackknife bias corrections. Finally, finite sample simulations are used to support our theoretical analysis and to illustrate the importance of bias correction in quantile regressions for panel data.
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PDF链接：
https://arxiv.org/pdf/1911.04729