摘要翻译：
本文研究了具有异质系数的线性面板回归模型的估计问题，当回归子和残差都可能含有共同的潜在因子结构时。我们的理论（几乎）是有效的，因为它基于GLS原理，而且对这种因子结构的说明也是稳健的，因为它不需要关于因子数目的任何信息，也不需要对因子结构本身的估计。我们首先展示了对于OLS受一阶偏差影响的情况，不可行的GLS估计器不仅提供了效率的提高，而且更重要的是，提供了一个具有传统极限分布的偏差调整估计器。本文解决的技术难题是如何在双渐近条件下证明一类可行GLS估计的这些性质。我们的理论是通过蒙特卡罗练习来说明的，然后用个人资产收益和企业特征数据进行了实证应用。
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英文标题：
《Robust Nearly-Efficient Estimation of Large Panels with Factor
  Structures》
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作者：
Marco Avarucci and Paolo Zaffaroni
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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：
  This paper studies estimation of linear panel regression models with heterogeneous coefficients, when both the regressors and the residual contain a possibly common, latent, factor structure. Our theory is (nearly) efficient, because based on the GLS principle, and also robust to the specification of such factor structure because it does not require any information on the number of factors nor estimation of the factor structure itself. We first show how the unfeasible GLS estimator not only affords an efficiency improvement but, more importantly, provides a bias-adjusted estimator with the conventional limiting distribution, for situations where the OLS is affected by a first-order bias. The technical challenge resolved in the paper is to show how these properties are preserved for a class of feasible GLS estimators in a double-asymptotics setting. Our theory is illustrated by means of Monte Carlo exercises and, then, with an empirical application using individual asset returns and firms' characteristics data.
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PDF链接：
https://arxiv.org/pdf/1902.11181