摘要翻译：
给出了基于低阶因子结构近似的不可分离面板模型的估计方法。利用矩阵补全法估计因子结构，解决了主成分分析在数据缺失情况下的计算难题。我们证明了所得到的估计量在大面板上是一致的，但受到近似和收缩偏差的影响。我们使用匹配和差异中的差异方法来纠正这些偏差。数值例子和对美国选举日登记对选民投票率影响的实证应用说明了我们方法的性质和有用性。
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英文标题：
《Low-Rank Approximations of Nonseparable Panel Models》
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作者：
Iv\'an Fern\'andez-Val, Hugo Freeman, Martin Weidner
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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：
  We provide estimation methods for nonseparable panel models based on low-rank factor structure approximations. The factor structures are estimated by matrix-completion methods to deal with the computational challenges of principal component analysis in the presence of missing data. We show that the resulting estimators are consistent in large panels, but suffer from approximation and shrinkage biases. We correct these biases using matching and difference-in-differences approaches. Numerical examples and an empirical application to the effect of election day registration on voter turnout in the U.S. illustrate the properties and usefulness of our methods.
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PDF链接：
https://arxiv.org/pdf/2010.12439