Detecting multiple mean breaks at unknown points in official time series

Authors:
Carmela Cappelli;Richard N. Penny;William S. Rea;Marco Reale
Affiliations:
Dipartimento di Scienze Statistiche, Universitá Federico II, Via Leopoldo Rodinó 22, 80138 Naples, Italy;Statistical Methods Division, Statistics New Zealand, Private Bag 4741, Christchurch, New Zealand;Mathematics and Statistics Department, University of Canterbury, Private Bag 4800, Christchurch, New Zealand;Mathematics and Statistics Department, University of Canterbury, Private Bag 4800, Christchurch, New Zealand
Venue:
Mathematics and Computers in Simulation
Year:
2008

Citing 1
Cited 2

Clustering Algorithms

Clustering Algorithms

Strong convergence rate of robust estimator of change point

Mathematics and Computers in Simulation
Long memory or shifting means in geophysical time series?

Mathematics and Computers in Simulation

Quantified Score

Hi-index	0.00

Visualization

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.