On the stability of recursive least squares in the Gauss-Markov model

Evens Salies

MPRA Paper from University Library of Munich, Germany

Abstract: In the Gauss-Markov regression model, one can always update the least square estimate of the slope vector, given new observations at the values of the explanatory variables. The updated estimate is often considered as a time-varying state of an auto-regressive system in Kalman filtering and recursive least squares theory. This note shows that the auto-regressive matrix of this dynamic system once centered has its largest eigenvalues equal to 1; the remaining eigenvalues are equal.

Keywords: Recursive Least Squares; Gauss-Markov model. (search for similar items in EconPapers)
JEL-codes: C3 C4 (search for similar items in EconPapers)
Date: 2004-09-06, Revised 2013-12-10
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https://mpra.ub.uni-muenchen.de/52116/1/MPRA_paper_52116.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/58036/1/MPRA_paper_52116.pdf revised version (application/pdf)

Related works:
Working Paper: On the stability of recursive least squares in the Gauss-Markov model (2004)
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