Recursive Least Squares Estimator with Multiple Exponential Windows in Vector Autoregression
Source: Acta Mathematicae Applicatae Sinica, Volume 18, Number 1, March 2002 , pp. 85-102(18)
Abstract:In the parameter tracking of time-varying systems, the ordinary method is weighted least squares with the rectangular window or the exponential window. In this paper we propose a new kind of sliding window called the multiple exponential window, and then use it to fit time-varying Gaussian vector autoregressive models. The asymptotic bias and covariance of the estimator of the parameter for time-invariant models are also derived. Simulation results show that the multiple exponential windows have better parameter tracking effect than rectangular windows and exponential ones.
Document Type: Research Article
Publication date: March 1, 2002