摘要翻译：
本文利用期望最大化算法和Kalman平滑器联合实现对具有共同趋势和特殊趋势的大动态因子模型的估计。我们证明了当截面维数$n$和样本容量$T$发散到无穷大时，给定单位在给定时间点估计的公共分量是$\min(\sqrt n,\sqrt T)$-一致的。还考虑了局部水平和/或局部线性趋势的情况。通过MonteCarlo模拟练习，我们将我们的方法与基于主成分分析的估计进行了比较。
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英文标题：
《Quasi Maximum Likelihood Estimation of Non-Stationary Large Approximate
  Dynamic Factor Models》
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作者：
Matteo Barigozzi and Matteo Luciani
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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 considers estimation of large dynamic factor models with common and idiosyncratic trends by means of the Expectation Maximization algorithm, implemented jointly with the Kalman smoother. We show that, as the cross-sectional dimension $n$ and the sample size $T$ diverge to infinity, the common component for a given unit estimated at a given point in time is $\min(\sqrt n,\sqrt T)$-consistent. The case of local levels and/or local linear trends trends is also considered. By means of a MonteCarlo simulation exercise, we compare our approach with estimators based on principal component analysis.
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PDF链接：
https://arxiv.org/pdf/1910.09841