MULTIVARIATE METHODS FOR MONITORING STRUCTURAL CHANGE
MULTIVARIATE METHODS FOR MONITORING STRUCTURAL CHANGE
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DOI：
10.1002/jae.1272
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发表时间：
2013-03
影响因子：
2.1
通讯作者：
Jan J. J. Groen-Jan-J.-J.-Groen-144866661;G. Kapetanios;S. Price
中科院分区：
文献类型：
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作者：
Jan J. J. Groen-Jan-J.-J.-Groen-144866661;G. Kapetanios;S. Price
结构变化的检测是一项关键的经验活动，但是连续的“监测”系列，实时的结构变化，引发了众所周知的计量经济学问题，这些问题已经在单一系列的背景下进行了探索。如果多个序列共断，那么同时检查一组序列可能比逐个检查序列更有可能或更快速地识别变化。对最大和平均CUSUM检测检验提出了一些渐近理论。蒙特卡罗实验表明，在给定足够大数量的共破序列的情况下，这两种方法都提供了相对于单变量检测器在大范围实验参数上的检测改进。这对误差的横截面相关性（一个因素结构）和断裂日期的异质性是稳健的。我们将该测试应用于一组英国价格指数。
Detection of structural change is a critical empirical activity, but continuous 'monitoring' of series, for structural changes in real time, raises well-known econometric issues that have been explored in a single series context. If multiple series co-break then it is possible that simultaneous examination of a set of series helps identify changes with higher probability or more rapidly than when series are examined on a case-by-case basis. Some asymptotic theory is developed for maximum and average CUSUM detection tests. Monte Carlo experiments suggest that these both provide an improvement in detection relative to a univariate detector over a wide range of experimental parameters, given a sufficiently large number of co-breaking series. This is robust to a cross-sectional correlation in the errors (a factor structure) and heterogeneity in the break dates. We apply the test to a panel of UK price indices.