摘要翻译：
本文提出了一种估算方法，利用从高块估算的因子和从宽块估算的再旋转载荷来估算一个数据面板中的缺失值。假设一个强因子结构对整个数据面板及其子块成立，证明了公共分量可以在四种不同的收敛速度下一致地估计，而不需要正则化或迭代。对估计误差进行了渐近分析。我们的分析的一个应用是当潜在的结果有一个因素结构时对反事实的估计。我们研究了平均和个体治疗效果对被治疗者的估计，并建立了一个正态分布理论，可以用于假设检验。
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英文标题：
《Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data》
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作者：
Jushan Bai and Serena Ng
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最新提交年份：
2021
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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 proposes an imputation procedure that uses the factors estimated from a tall block along with the re-rotated loadings estimated from a wide block to impute missing values in a panel of data. Assuming that a strong factor structure holds for the full panel of data and its sub-blocks, it is shown that the common component can be consistently estimated at four different rates of convergence without requiring regularization or iteration. An asymptotic analysis of the estimation error is obtained. An application of our analysis is estimation of counterfactuals when potential outcomes have a factor structure. We study the estimation of average and individual treatment effects on the treated and establish a normal distribution theory that can be useful for hypothesis testing.
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PDF链接：
https://arxiv.org/pdf/1910.06677