Asymptotic normality of wavelet covariances and of multivariate wavelet Whittle estimators
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Publication:6356314
DOI10.1016/J.SPA.2022.10.012arXiv2012.09436MaRDI QIDQ6356314
Publication date: 17 December 2020
Abstract: Multivariate processes with long-range dependence properties can be encountered in many fields of application. Two fundamental characteristics in such frameworks are long-range dependence parameters and correlations between component time series. We consider multivariate long-range dependent linear processes, not necessarily Gaussian. We show that the covariances between the wavelet coefficients in this setting are asymptotically Gaussian. We also study the asymptotic distributions of the estimators of the long-range dependence parameter and the long-run covariance by a wavelet-based Whittle procedure. We prove the asymptotic normality of the estimators, and we provide an explicit expression for the asymptotic covariances. An empirical illustration of this result is proposed on a real dataset of rat brain connectivity.
Asymptotic properties of nonparametric inference (62G20) Nontrigonometric harmonic analysis involving wavelets and other special systems (42C40) Inference from stochastic processes and spectral analysis (62M15)
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