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Asymptotics of empirical processes of long memory moving averages with infinite variance

Listed author(s):
  • Koul, Hira L.
  • Surgailis, Donatas
Registered author(s):

    This paper obtains a uniform reduction principle for the empirical process of a stationary moving average time series {Xt} with long memory and independent and identically distributed innovations belonging to the domain of attraction of symmetric [alpha]-stable laws, 1

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    File URL: http://www.sciencedirect.com/science/article/pii/S0304-4149(00)00065-X
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    Article provided by Elsevier in its journal Stochastic Processes and their Applications.

    Volume (Year): 91 (2001)
    Issue (Month): 2 (February)
    Pages: 309-336

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    Handle: RePEc:eee:spapps:v:91:y:2001:i:2:p:309-336
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    1. Giraitis, Liudas & Koul, Hira L. & Surgailis, Donatas, 1996. "Asymptotic normality of regression estimators with long memory errors," Statistics & Probability Letters, Elsevier, vol. 29(4), pages 317-335, September.
    2. Koul, Hira L., 1992. "M-estimators in linear models with long range dependent errors," Statistics & Probability Letters, Elsevier, vol. 14(2), pages 153-164, May.
    3. Kokoszka, Piotr S. & Taqqu, Murad S., 1995. "Fractional ARIMA with stable innovations," Stochastic Processes and their Applications, Elsevier, vol. 60(1), pages 19-47, November.
    4. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    5. Knight, Keith, 1993. "Estimation in Dynamic Linear Regression Models with Infinite Variance Errors," Econometric Theory, Cambridge University Press, vol. 9(04), pages 570-588, August.
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