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A0721
Title: Functional estimation and change detection for nonstationary time series Authors:  Fabian Mies - RWTH Aachen University (Germany) [presenting]
Abstract: Tests for structural breaks in time series should ideally be sensitive to breaks in the parameter of interest while being robust to nuisance changes. Thus, the statistical analysis needs to allow for some form of nonstationarity under the null hypothesis of no change. We construct estimators for integrated parameters of locally stationary time series. A corresponding functional central limit theorem is established, enabling change-point inference for a broad class of parameters under mild assumptions. The proposed framework covers all parameters that may be expressed as nonlinear functions of moments, such as kurtosis, autocorrelation, and coefficients in a linear regression model. A bootstrap variant is proposed to perform feasible inference based on the derived limit distribution, and its consistency is established. The methodology is illustrated through a simulation study and by an application to high-frequency asset prices.