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hal-00271136, version 1

On asymptotic normality of sequential LS-estimate for unstable autoregressive process AR(2).

Leonid Galtchouk () 1, Victor Konev 2

Abstract: For estimating parameters in an unstable AR(2) model, the paper proposes a sequential least squares estimate with a special stopping time defined by the trace of the observed Fisher information matrix. It is shown that the sequential LSE is asymptotically normally distributed in the stability region and on its boundary in contrast to the usual LSE, having six different types of asymptotic distributions on the boundary depending on the values of the unknown parameters.

  • 1:  Institut de Recherche Mathématique Avancée (IRMA)
  • CNRS : UMR7501 – Université Louis Pasteur - Strasbourg I
  • 2:  Université de Tomsk
  • Russian Academy of Science
  • Domain : Mathematics/Statistics
    Statistics/Statistics Theory
  • Keywords : Autoregressive process – least squares estimate – sequential estimation – asymptotic normality.
 
  • hal-00271136, version 1
  • http://hal.archives-ouvertes.fr/hal-00271136
  • oai:hal.archives-ouvertes.fr:hal-00271136
  • From: Leonid Galtchouk <>
  • Submitted on: Tuesday, 8 April 2008 12:13:46
  • Updated on: Tuesday, 30 September 2008 13:13:18