Detecting multiple change-points in general causal time series using penalized quasi-likelihood

Details Detecting multiple change-points in general causal time series using penalized quasi-likelihood Detecting multiple change-points in general causal time series using penalized quasi-likelihood

2010-07-31

arxiv.org/abs/1008.0054v1

Mathematics

Statistics Theory

Scientific paper

This paper is devoted to the off-line multiple change-point detection in a semiparametric framework. The time series is supposed to belong to a large class of models including AR($\infty$), ARCH($\infty$), TARCH($\infty$),... models where the coefficients change at each instant of breaks. The different unknown parameters (number of changes, change dates and parameters of successive models) are estimated using a penalized contrast built on conditional quasi-likelihood. Under Lipshitzian conditions on the model, the consistency of the estimator is proved when the moment order $r$ of the process satisfies $r\geq 2$. If $r\geq 4$, the same convergence rates for the estimators than in the case of independent random variables are obtained. The particular cases of AR($\infty$), ARCH($\infty$) and TARCH($\infty$) show that our method notably improves the existing results.

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