Detecting multiple mean breaks at unknown points in official time series

被引：16
|
作者：
Cappelli, Carmela [2 ]
Penny, Richard N. [3 ]
Rea, William S. [1 ]
Reale, Marco [1 ]
机构：
[1] Univ Canterbury, Dept Math & Stat, Christchurch 1, New Zealand
[2] Univ Naples Federico 2, Dipartimento Sci Stat, I-80138 Naples, Italy
[3] Stat New Zealand, Stat Methods Div, Christchurch 1, New Zealand
来源：
MATHEMATICS AND COMPUTERS IN SIMULATION | 2008年 / 78卷 / 2-3期
关键词：
partitioning; regression trees; X-12; ARIMA;
D O I：
10.1016/j.matcom.2008.01.041
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
In this paper, we propose a computationally effective approach to detect multiple structural breaks in the mean occurring at unknown dates. We present a non-parametric approach that exploits, in the framework of least squares regression trees, the contiguity property of data generating processes in time series data. The proposed approach is applied first to simulated data and then to the Quarterly Gross Domestic Product in New Zealand to assess some of anomalous observations indicated by the seasonal adjustment procedure implemented in X12-ARIMA are actually structural breaks. (C) 2008 IMACS. Published by Elsevier B.V. All rights reserved.
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页码：351 / 356
页数：6
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