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. © 2008 IMACS.
Detecting multiple mean breaks at unknown points in official time series / Cappelli, Carmela; R., Penny; W., Rea; M., Reale. - In: MATHEMATICS AND COMPUTERS IN SIMULATION. - ISSN 0378-4754. - STAMPA. - 78:2-3(2008), pp. 351-356. [10.1016/j.matcom.2008.01.041]
Detecting multiple mean breaks at unknown points in official time series
CAPPELLI, CARMELA;
2008
Abstract
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. © 2008 IMACS.| File | Dimensione | Formato | |
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