Maximum likelihood estimators in regression models with infinite variance innovations

Vygantas Paulaauskas, Svetlozar T. Rachev

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

In this paper we consider the problem of maximum likelihood (ML) estimation in the classical AR(1) model with i.i.d. symmetric stable innovations with known characteristic exponent and unknown scale parameter. We present an approach that allows us to investigate the properties of ML estimators without making use of numerical procedures. Finally, we introduce a generalization to the multivariate case.

Original languageEnglish
Pages (from-to)47-65
Number of pages19
JournalStatistical Papers
Volume44
Issue number1
DOIs
StatePublished - Jan 2003

Keywords

  • Autoregression
  • Lévy processes
  • Maximum likelihood estimators
  • Stable distributions

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