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CSDA
2007

Wavelet based time-varying vector autoregressive modelling

11 years 11 months ago
Wavelet based time-varying vector autoregressive modelling
Vector autoregressive (VAR) modelling is one of the most popular approaches in multivariate time series analysis. The parameters interpretation is simple, and provide an intuitive identification of relationships and Granger causality among time series. However, the VAR modelling requires stationarity conditions which could not be valid in many practical applications. Locally stationary or time dependent modelling seem attractive generalizations, and several univariate approaches have already been proposed. In this paper we propose an estimation procedure for time-varying vector autoregressive processes, based on wavelet expansions of autoregressive coefficients. The asymptotic properties of the estimator are derived and illustrated by computer intensive simulations. We also present an application to brain connectivity identification using functional magnetic resonance imaging (fMRI) datasets. Key words: Wavelets, time-varying, autoregressive, multivariate
João Ricardo Sato, Pedro Alberto Morettin,
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2007
Where CSDA
Authors João Ricardo Sato, Pedro Alberto Morettin, Paula R. Arantes, Edson Amaro Jr.
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