Maximum likelihood estimators in regression models with infinite variance innovations

Vygantas Paulaauskas; Svetlozar T. Rachev

doi:10.1007/s00362-002-0133-8

Maximum likelihood estimators in regression models with infinite variance innovations

Vygantas Paulaauskas, Svetlozar T. Rachev

Mathematics and Statistics

Research output: Contribution to journal › Article › peer-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 language	English
Pages (from-to)	47-65
Number of pages	19
Journal	Statistical Papers
Volume	44
Issue number	1
DOIs	https://doi.org/10.1007/s00362-002-0133-8
State	Published - Jan 2003

Keywords

Autoregression
Lévy processes
Maximum likelihood estimators
Stable distributions

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10.1007/s00362-002-0133-8

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KW - Stable distributions

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