动态因子模型中的离群点

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摘要翻译：
动态因素模型在计量经济学和应用经济学中有着广泛的应用。其基本动机在于他们能够将大量的时间序列缩减为仅有的几个指标（因素）。如果时间序列的数量与现有的观测数量相比较大，那么大多数信息可能被传递给因子。通过这种方式，可以估计低维模型来解释和预测一个或多个感兴趣的时间序列。期望无离群值的时间序列能够用于估计。在实践中，由于外部异常事件或数据输入严重错误等原因，可能在未知的日期出现外围观测。时间序列中的离群点检测有几种方法可供选择。然而，大多数方法都适用于单变量时间序列，而即使是为处理多变量框架而设计的方法也没有明确地包括动态因素模型。介绍了一种基于观测数据线性变换的动态因子模型异常点发现方法。讨论了一些分离添加到模型中的离群点和公共组件中的离群点的策略。通过对模拟和真实数据集的应用，验证了该方法的有效性。
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英文标题：
《Outliers in dynamic factor models》
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作者：
Roberto Baragona, Francesco Battaglia
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最新提交年份：
2007
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分类信息：

一级分类：Mathematics        数学
二级分类：Statistics Theory        统计理论
分类描述：Applied, computational and theoretical statistics: e.g. statistical inference, regression, time series, multivariate analysis, data analysis, Markov chain Monte Carlo, design of experiments, case studies
应用统计、计算统计和理论统计：例如统计推断、回归、时间序列、多元分析、数据分析、马尔可夫链蒙特卡罗、实验设计、案例研究
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一级分类：Statistics        统计学
二级分类：Statistics Theory        统计理论
分类描述：stat.TH is an alias for math.ST. Asymptotics, Bayesian Inference, Decision Theory, Estimation, Foundations, Inference, Testing.
Stat.Th是Math.St的别名。渐近，贝叶斯推论，决策理论，估计，基础，推论，检验。
--

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英文摘要：
  Dynamic factor models have a wide range of applications in econometrics and applied economics. The basic motivation resides in their capability of reducing a large set of time series to only few indicators (factors). If the number of time series is large compared to the available number of observations then most information may be conveyed to the factors. This way low dimension models may be estimated for explaining and forecasting one or more time series of interest. It is desirable that outlier free time series be available for estimation. In practice, outlying observations are likely to arise at unknown dates due, for instance, to external unusual events or gross data entry errors. Several methods for outlier detection in time series are available. Most methods, however, apply to univariate time series while even methods designed for handling the multivariate framework do not include dynamic factor models explicitly. A method for discovering outliers occurrences in a dynamic factor model is introduced that is based on linear transforms of the observed data. Some strategies to separate outliers that add to the model and outliers within the common component are discussed. Applications to simulated and real data sets are presented to check the effectiveness of the proposed method.
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PDF链接：
https://arxiv.org/pdf/710.3676

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关键词：动态因子模型 动态因子 Multivariate Applications observations factor 数据 数量 data 讨论