Recursive Least Squares Estimator with Multiple Exponential Windows in Vector Autoregression

Authors: An, Hong-zhi1; Li, Zhi-guo2

Source: Acta Mathematicae Applicatae Sinica, Volume 18, Number 1, March 2002 , pp. 85-102(18)

Publisher: Springer

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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.

Keywords: 62G10; 62M10; Exponential window; multiple exponential window; rectangular window; vector autoregression; weighted least squares method

Document Type: Research Article


Affiliations: 1: Email: 2: Email:

Publication date: March 1, 2002

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