Asymptotic Expansion of the Empirical Process of Long Memory Moving Averages

Hira L. Koul () and Donatas Surgailis ()
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Hira L. Koul: Michigan State University, Department of Statistics and Probability
Donatas Surgailis: Vilnius Institute of Mathematics and Informatics

A chapter in Empirical Process Techniques for Dependent Data, 2002, pp 213-239 from Springer

Abstract: 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: Asymptotic Expansion; Empirical Process; Memory Error; Gaussian Case; Infinite Variance (search for similar items in EconPapers)
Date: 2002
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4612-0099-4_7

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DOI: 10.1007/978-1-4612-0099-4_7

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