Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data
Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data
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DOI：
10.1080/01621459.2021.1967163
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发表时间：
2021-09-17
影响因子：
3.7
通讯作者：
Ng, Serena
中科院分区：
文献类型：
--
作者：
Bai, Jushan;Ng, Serena
本文提出了一种插补方法，该方法使用从高块沿着估计的因子以及从宽块估计的重新旋转载荷来插补数据组中的缺失值。假设一个强大的因素结构，持有的数据和其子块的整个面板，它表明，共同的组成部分可以一致地估计在四个不同的收敛速度，而不需要正则化或迭代。估计误差的渐近分析。我们的分析的一个应用是估计反事实时，潜在的结果有一个因素结构。我们研究了平均和个体治疗效果的估计，并建立了一个正态分布理论，可以用于假设检验。
This article 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.