Interventions in INGARCH processes
Interventions in INGARCH processes
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DOI：
10.1111/j.1467-9892.2010.00657.x
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发表时间：
2010-05-01
影响因子：
0.9
通讯作者：
Fried, Roland
中科院分区：
文献类型：
--
作者：
Fokianos, Konstantinos;Fried, Roland
研究了线性计数时间序列模型中干预效应产生各种异常值的问题。该模型属于观测驱动模型类，并在指数族框架内扩展了高斯线性时间序列模型类。关于计数时间序列模型的协变量和干预措施的影响的研究在很大程度上已经落后，因为没有观察到其行为决定所观察到的过程的动态的基本过程。我们提出了一个计算上可行的方法来解决这些问题，特别是侧重于检测和估计的突然变化和离群值。我们考虑三种不同的情况下，即在一个已知的时间检测一个已知类型的干预效果，检测的干预效果时，类型和时间都是未知的和多个干预效果的检测。我们开发了第一种情况下的分数测试和参数的bootstrap程序的基础上的最大值的不同的分数测试统计量的第二种情况。第三种情况是通过逐步的过程来处理的，在这里我们迭代地检测和纠正干预效果。使用模拟和真实的数据的例子说明所提出的方法的实用性。
We study the problem of intervention effects generating various types of outliers in a linear count time-series model. This model belongs to the class of observation-driven models and extends the class of Gaussian linear time-series models within the exponential family framework. Studies about effects of covariates and interventions for count time-series models have largely fallen behind, because the underlying process, whose behaviour determines the dynamics of the observed process, is not observed. We suggest a computationally feasible approach to these problems, focusing especially on the detection and estimation of sudden shifts and outliers. We consider three different scenarios, namely the detection of an intervention effect of a known type at a known time, the detection of an intervention effect when the type and the time are both unknown and the detection of multiple intervention effects. We develop score tests for the first scenario and a parametric bootstrap procedure based on the maximum of the different score test statistics for the second scenario. The third scenario is treated by a stepwise procedure, where we detect and correct intervention effects iteratively. The usefulness of the proposed methods is illustrated using simulated and real data examples.