英文标题：
《Sequential testing for structural stability in approximate factor models》
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作者：
Matteo Barigozzi, Lorenzo Trapani
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最新提交年份：
2020
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英文摘要：
We develop a monitoring procedure to detect changes in a large approximate factor model. Letting $r$ be the number of common factors, we base our statistics on the fact that the $\\left( r+1\\right) $-th eigenvalue of the sample covariance matrix is bounded under the null of no change, whereas it becomes spiked under changes. Given that sample eigenvalues cannot be estimated consistently under the null, we randomise the test statistic, obtaining a sequence of \\textit{i.i.d} statistics, which are used for the monitoring scheme. Numerical evidence shows a very small probability of false detections, and tight detection times of change-points.
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中文摘要：
我们开发了一个监测程序，以检测大近似因子模型中的变化。假设$r$是公因子数，我们的统计数据基于这样一个事实，即样本协方差矩阵的$\\左（r+1 \\右）$-第个特征值在零无变化的情况下有界，而在变化的情况下会变得尖峰。假设样本特征值不能在空值下一致估计，我们将测试统计量随机化，获得一系列用于监测方案的{i.i.d}统计量。数值证据表明，错误检测的概率很小，变化点的检测时间很短。
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分类信息：

一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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