A1099
Title: Estimation of the long-run variance of nonlinear time series with an application to change point analysis
Authors: Vaidotas Characiejus - University of Southern Denmark (Denmark) [presenting]
Piotr Kokoszka - Colorado State University (USA)
Xiangdong Meng - Colorado State University (United States)
Abstract: For a broad class of nonlinear time series known as Bernoulli shifts, the asymptotic normality of the smoothed periodogram estimator of the long-run variance is established. This estimator uses only a narrow band of Fourier frequencies around the origin and so has been extensively used in local Whittle estimation. Existing asymptotic normality results apply only to linear time series, so substantially extending the scope of the applicability of the smoothed periodogram estimator. As an illustration, it is applied to a test of changes in mean against long-range dependence. A simulation study is also conducted to illustrate the performance of the test for nonlinear time series.
