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Asymptotic Expansion of the Empirical Process of Long Memory Moving Averages

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Empirical Process Techniques for Dependent Data

Abstract

Moving averages in i.i.d. variables form one of the most important classes of long memory time series. The paper reviews various results on the asymptotic distribution of empirical processes of long memory moving averages with finite and infinite variance. It also discusses some interesting applications to goodness-of-fit testing for the marginal stationary error distribution in linear regression models and M-estimation in the one sample location model.

Research of this author was partly supported by the NSF grant DMS 0071619.

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Author information

Authors and Affiliations

  1. Department of Statistics and Probability, Michigan State University, East Lansing, MI, 48824-1027, USA

    Hira L. Koul

  2. Vilnius Institute of Mathematics and Informatics, Akademijos 4, 2600, Vilnius, Lithuania

    Donatas Surgailis

Authors
  1. Hira L. Koul
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  2. Donatas Surgailis
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Editors and Affiliations

  1. Fakultät für Mathematik, Ruhr-Universität Bochum, 44780, Bochum, Germany

    Herold Dehling

  2. Laboratory of Actuarial Mathematics, University of Copenhagen, DK-2100, Copenhagen, Denmark

    Thomas Mikosch

  3. Department of Statistics and Operations Research, University of Copenhagen, DK-2100, Copenhagen, Denmark

    Michael Sørensen

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Koul, H.L., Surgailis, D. (2002). Asymptotic Expansion of the Empirical Process of Long Memory Moving Averages. In: Dehling, H., Mikosch, T., Sørensen, M. (eds) Empirical Process Techniques for Dependent Data. Birkhäuser, Boston, MA. https://doi.org/10.1007/978-1-4612-0099-4_7

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  • DOI: https://doi.org/10.1007/978-1-4612-0099-4_7

  • Publisher Name: Birkhäuser, Boston, MA

  • Print ISBN: 978-1-4612-6611-2

  • Online ISBN: 978-1-4612-0099-4

  • eBook Packages: Springer Book Archive

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