Detecting multiple mean breaks at unknown points in official time series

Carmela Cappelli, Richard N. Penny, William Rea and Marco Reale

Mathematics and Computers in Simulation (MATCOM), 2008, vol. 78, issue 2, 351-356

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.

Keywords: Partitioning; Regression trees; X-12 ARIMA (search for similar items in EconPapers)
Date: 2008
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378475408000475
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:78:y:2008:i:2:p:351-356

DOI: 10.1016/j.matcom.2008.01.041

Access Statistics for this article

Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens

More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier
Bibliographic data for series maintained by Catherine Liu ().