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英文文献：Detection of additive outliers in seasonal time series-季节时间序列中附加异常值的检测
英文文献作者：Niels Haldrup,Antonio Monta?és,Andreu Sansó
英文文献摘要：
The detection and location of additive outliers in integrated variables has attracted much attention recently because such outliers tend to affect unit root inference among other things. Most of these procedures have been developed for non-seasonal processes. However, the presence of seasonality in the form of seasonally varying means and variances affect the properties of outlier detection procedures, and hence appropriate adjustments of existing methods are needed for seasonal data. In this paper we suggest modifications of tests proposed by Shin et al. (1996) and Perron and Rodriguez (2003) to deal with data sampled at a seasonal frequency and the size and power properties are discussed. We also show that the presence of periodic heteroscedasticity will inflate the size of the tests and hence will tend to identify an excessive number of outliers. A modified Perron-Rodriguez test which allows periodically varying variances is suggested and it is shown to have excellent properties in terms of both power and size.
由于积分变量中附加异常值的检测和定位往往会影响到单位根的推断等问题，近年来引起了广泛的关注。大多数这些程序是为非季节性程序而开发的。但是，季节性变化的均值和方差的存在影响了离群值检测程序的特性，因此需要对季节性数据对现有方法进行适当的调整。在本文中，我们建议修改Shin等人(1996)和Perron和Rodriguez(2003)提出的测试，以处理在季节频率采样的数据，并讨论了大小和功率特性。我们还表明，周期性异方差的存在将扩大测试的规模，因此将倾向于识别过多的异常值。提出了一种允许周期性变化方差的改进Perron-Rodriguez试验，并证明了它在功率和尺寸方面都具有优异的性能。
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