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摘要翻译：
本文发展了基于缺失观测的大维面板数据估计潜在因子模型的推理理论。利用主成分分析方法对部分观测面板数据估计的协方差矩阵进行调整，提出了一种易于使用的潜在因素模型的通用估计器。在近似因子模型和一般缺失模式下，导出了估计因子、载荷和估算值的渐近分布。其关键应用是从面板数据中估计因果推断中的反事实结果。未观察的对照组被建模为缺失值，这些缺失值是从潜在因素模型中推断出来的。估算值的推理理论允许我们在一般采用模式下的任何时候测试个体治疗效果，在这种模式下，单位可能受到未观察到的因素的影响。
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英文标题：
《Large Dimensional Latent Factor Modeling with Missing Observations and
Applications to Causal Inference》
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作者：
Ruoxuan Xiong and Markus Pelger
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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 develops the inferential theory for latent factor models estimated from large dimensional panel data with missing observations. We propose an easy-to-use all-purpose estimator for a latent factor model by applying principal component analysis to an adjusted covariance matrix estimated from partially observed panel data. We derive the asymptotic distribution for the estimated factors, loadings and the imputed values under an approximate factor model and general missing patterns. The key application is to estimate counterfactual outcomes in causal inference from panel data. The unobserved control group is modeled as missing values, which are inferred from the latent factor model. The inferential theory for the imputed values allows us to test for individual treatment effects at any time under general adoption patterns where the units can be affected by unobserved factors.
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PDF链接：
https://arxiv.org/pdf/1910.08273
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