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Detecting multiple mean breaks at unknown points in official time series. (English) Zbl 1143.65009
Summary: 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.

MSC:
65C60 Computational problems in statistics (MSC2010)
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
62P20 Applications of statistics to economics
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