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.
Detecting multiple mean mreaks at unknown pointswith atheoretical regression trees / Cappelli, C., R. N., P., M., R.. - ELETTRONICO. - (2005), pp. 975-978. (International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand australia dicembre 2005).
Detecting multiple mean mreaks at unknown pointswith atheoretical regression trees
CAPPELLI, CARMELA;
2005
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
