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BIAS AND COVARIANCE OF THE RECURSIVE LEAST SQUARES ESTIMATOR WITH EXPONENTIAL FORGETTING IN VECTOR AUTOREGRESSIONS
Author(s) -
Lindoff B.,
Holst J.
Publication year - 1996
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.1996.tb00293.x
Subject(s) - mathematics , estimator , covariance , exponential function , recursive least squares filter , gaussian , least squares function approximation , statistics , algorithm , mathematical analysis , adaptive filter , physics , quantum mechanics
. The recursive least squares (RLS) estimation algorithm with exponential forgetting is commonly used to estimate time‐varying parameters in stochastic systems. The statistical properties of the RLS estimator are often hard to find, since they depend in a non‐linear way on the time‐varying characteristics. In this paper the RLS estimator with exponential forgetting factor is applied to stationary Gaussian vector autoregres‐sions and the asymptotic bias and covariance function of the parameter estimates are derived.