摘要翻译：
具有固定个体效应的非线性面板数据模型为描述微观计量数据提供了一套重要的工具。在一大类这样的模型中（包括probit模型、比例危险模型和分位数回归模型，仅举几例），不可能区分出个别效应，而推论通常是在“大n大T”渐近框架中进行的。然而，目前在具有平滑得分函数（如probit和比例风险）和分位数回归的模型中施加的假设类型存在相当大的差距。在本文中，我们证明了在n，T上的条件下，分位元回归面板可以弥合这一间隙，并建立了渐近无偏正态性，这些条件非常接近于标准非线性面板中的典型假定。我们的结果在很大程度上改进了现有的理论，并表明分位数回归与其他常用的非线性面板数据模型一样适用于相同类型的面板数据（就n,T而言）。深入的数值实验证实了我们的理论发现。
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英文标题：
《On the Unbiased Asymptotic Normality of Quantile Regression with Fixed
  Effects》
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作者：
Antonio F. Galvao, Jiaying Gu, Stanislav Volgushev
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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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一级分类：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
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英文摘要：
  Nonlinear panel data models with fixed individual effects provide an important set of tools for describing microeconometric data. In a large class of such models (including probit, proportional hazard and quantile regression to name just a few) it is impossible to difference out individual effects, and inference is usually justified in a `large n large T' asymptotic framework. However, there is a considerable gap in the type of assumptions that are currently imposed in models with smooth score functions (such as probit, and proportional hazard) and quantile regression. In the present paper we show that this gap can be bridged and establish asymptotic unbiased normality for quantile regression panels under conditions on n,T that are very close to what is typically assumed in standard nonlinear panels. Our results considerably improve upon existing theory and show that quantile regression is applicable to the same type of panel data (in terms of n,T) as other commonly used nonlinear panel data models. Thorough numerical experiments confirm our theoretical findings.
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PDF链接：
https://arxiv.org/pdf/1807.11863