Detecting Multiple Mean Breaks At Unknown Points With Atheoretical Regression Trees
نویسندگان
چکیده
In this paper we propose a computationally effective approach to detect multiple structural breaks in the mean occurring at unknown dates. We propose a non-parametric approach that exploits, in the framework of least squares regression trees, the contiguity property of the Fisher grouping method (1958) proposed for grouping a single real variable. The proposed approach is applied to study the possibility of using the series of anomalous observation C17 provided by the seasonal adjustment procedure implemented in X12-ARIMA.
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