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

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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(∞), ARCH(∞), TARCH(∞),... 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≥ 2. If r≥ 4, the same convergence rates for the estimators than in the case of independent random variables are obtained. The particular cases of AR(∞), ARCH(∞) and TARCH(∞) show that our method notably improves the existing results.