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Maximum likelihood estimators in regression models with infinite variance innovations

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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.

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Authors and Affiliations

  1. Department of Mathematics, Vilnius University, Vilnius, Lithuania

    Vygantas Paulaauskas

  2. Institut of Statistics and Mathematical Economics, Department of Economics, University of Karlsrhue, Kollegium am Schloss, D-76128, Karlsruhe, Germany

    Svetlozar T. Rachev

  3. Department of Statistics and Applied Probability, University of California, Santa Barbara, CA, USA

    Svetlozar T. Rachev

Authors
  1. Vygantas Paulaauskas
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  2. Svetlozar T. Rachev
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Correspondence to Vygantas Paulaauskas.

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Paulaauskas, V., Rachev, S.T. Maximum likelihood estimators in regression models with infinite variance innovations. Statistical Papers 44, 47–65 (2003). https://doi.org/10.1007/s00362-002-0133-8

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  • DOI: https://doi.org/10.1007/s00362-002-0133-8

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