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A Kalman filter based approach for estimating nonstationary VAR models via pole tracking

A time-varying vector autoregressive (VAR) model is used for the modeling of time series with changing spectral content. Our approach focuses on the model's time-varying poles. We show the relationship between the deviations in these poles and deviations in the VAR coefficients, which leads to a reparameterization of the model. The performance characteristics of the model are investigated by using simulation.