Asymptotic Expansion of the Empirical Process of Long Memory Moving Averages

  • Hira L. Koul
  • Donatas Surgailis

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.

Keywords

Covariance 

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Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • Hira L. Koul
    • 1
  • Donatas Surgailis
    • 2
  1. 1.Department of Statistics and ProbabilityMichigan State UniversityEast LansingUSA
  2. 2.Vilnius Institute of Mathematics and InformaticsVilniusLithuania

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