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摘要翻译：
本文考虑因变量的条件分位数作为回归函数和个体效应的未知函数可加性分离的面板数据模型。在控制个体异质性的情况下，我们提出了分位数部分效应的两个估计。第一种估计量是基于局部线性分位数回归的，第二种估计量是基于局部线性平滑分位数回归的，这两种估计量在实际中都很容易计算。在大T框架下，我们给出了两个估计量渐近正态分布的充分条件。特别地，对于第一个估计量，证明了忽略附带参数偏差需要$n<<t^{2/(d+4)}，其中$d$是回归子的维数。对于第二种估计量，我们可以在$n\大约th^{d}$的假设下导出渐近偏差的解析表达式，其中$h$是局部线性近似中的带宽参数。我们的理论结果为使用分面板折刀进行偏置校正提供了依据。Monte Carlo仿真表明，所提出的估计器和偏置校正方法在有限样本中具有良好的性能。
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英文标题：
《Nonparametric Quantile Regressions for Panel Data Models with Large T》
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作者：
Liang Chen
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最新提交年份：
2020
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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：
This paper considers panel data models where the conditional quantiles of the dependent variables are additively separable as unknown functions of the regressors and the individual effects. We propose two estimators of the quantile partial effects while controlling for the individual heterogeneity. The first estimator is based on local linear quantile regressions, and the second is based on local linear smoothed quantile regressions, both of which are easy to compute in practice. Within the large T framework, we provide sufficient conditions under which the two estimators are shown to be asymptotically normally distributed. In particular, for the first estimator, it is shown that $N<<T^{2/(d+4)}$ is needed to ignore the incidental parameter biases, where $d$ is the dimension of the regressors. For the second estimator, we are able to derive the analytical expression of the asymptotic biases under the assumption that $N\approx Th^{d}$, where $h$ is the bandwidth parameter in local linear approximations. Our theoretical results provide the basis of using split-panel jackknife for bias corrections. A Monte Carlo simulation shows that the proposed estimators and the bias-correction method perform well in finite samples.
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PDF链接：
https://arxiv.org/pdf/1911.01824
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