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Bias And Covariance Of The Recursive Least Squares Estimator With Exponential Forgetting In Vector Autoregressions

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  • B. Lindoff
  • J. Holst

Abstract

. 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.

Suggested Citation

  • B. Lindoff & J. Holst, 1996. "Bias And Covariance Of The Recursive Least Squares Estimator With Exponential Forgetting In Vector Autoregressions," Journal of Time Series Analysis, Wiley Blackwell, vol. 17(6), pages 553-570, November.
    • Handle: RePEc:bla:jtsera:v:17:y:1996:i:6:p:553-570
    DOI: 10.1111/j.1467-9892.1996.tb00293.x
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    Cited by:

    1. Lindoff, B., 1998. "First inverse moment of a generalized quadratic form," Statistics & Probability Letters, Elsevier, vol. 40(4), pages 363-370, November.

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