动态因子模型中的异常值
Outliers in dynamic factor models
摘要：
动态因子模型在计量经济学和应用经济学中有着广泛的应用。其基本动机在于它们有能力将大量的时间序列缩减为几个指标（因素）。如果时间序列的数量与观测的可用数量相比较大，则大多数信息可以被传送到因子。这样，低维模型可以被估计用于解释和预测一个或多个感兴趣的时间序列。期望无异常值的时间序列可用于估计。在实践中，由于外部异常事件或严重的数据输入错误等原因，可能会在未知日期出现异常观察结果。时间序列中异常值检测的几种方法是可用的。然而，大多数方法适用于单变量时间序列，而即使是设计用于处理多变量框架的方法也没有明确地包括动态因子模型。提出了一种基于观测数据线性变换的动态因子模型中异常点的发现方法。讨论了一些分离加入模型的离群值和公共分量内离群值的策略。模拟和真实数据集的应用程序来检查所提出的方法的有效性。
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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