Salies, Evens (2004): On the stability of recursive least squares in the GaussMarkov model.
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Abstract
In the GaussMarkov 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 timevarying state of an autoregressive system in Kalman filtering and recursive least squares theory. This note shows that the autoregressive matrix of this dynamic system once centered has its largest eigenvalues equal to 1 and one eigenvalue that is less than 1.
Item Type:  MPRA Paper 

Original Title:  On the stability of recursive least squares in the GaussMarkov model 
Language:  English 
Keywords:  Recursive Least Squares, GaussMarkov model. 
Subjects:  C  Mathematical and Quantitative Methods > C3  Multiple or Simultaneous Equation Models ; Multiple Variables C  Mathematical and Quantitative Methods > C4  Econometric and Statistical Methods: Special Topics 
Item ID:  58036 
Depositing User:  Evens Salies 
Date Deposited:  21 Aug 2014 07:48 
Last Modified:  10 Oct 2019 04:39 
References:  Harvey, A. C. (1990). The Econometric analysis of time series. 2nd ed., Cambridge, MA: MIT Press. Kianifard F., and Swallow, H. (1996). A review of the development and application of recursive residuals in linear models. Journal of the American Statistical Association, 91(433), pp. 391400. Magnus, J. R. and Neudecker, H. (1991). Matrix Differential Calculus with Applications in Statistics and Econometrics. John Wiley. Printed in Great Britain. 
URI:  https://mpra.ub.unimuenchen.de/id/eprint/58036 
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On the stability of recursive least squares in the GaussMarkov model. (deposited 11 Dec 2013 09:19)
 On the stability of recursive least squares in the GaussMarkov model. (deposited 21 Aug 2014 07:48) [Currently Displayed]