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On Koul's minimum distance estimators in the regression models with long memory moving averages

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  • Li, Linyuan

Abstract

This paper discusses the asymptotic behavior of Koul's minimum distance estimators of the regression parameter vector in linear regression models with long memory moving average errors, when the design variables are known constants. It is observed that all these estimators are asymptotically equivalent to the least-squares estimator in the first order.

Suggested Citation

  • Li, Linyuan, 2003. "On Koul's minimum distance estimators in the regression models with long memory moving averages," Stochastic Processes and their Applications, Elsevier, vol. 105(2), pages 257-269, June.
    • Handle: RePEc:eee:spapps:v:105:y:2003:i:2:p:257-269
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    References listed on IDEAS

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    1. Javier Hidalgo & Peter M Robinson, 1997. "Time Series Regression with Long Range Dependence - (Now published in 'Annals of Statistics', 25, (1997)pp.2054-2083.)," STICERD - Econometrics Paper Series 318, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    2. Koul, Hira L., 1985. "Minimum distance estimation in linear regression with unknown error distributions," Statistics & Probability Letters, Elsevier, vol. 3(1), pages 1-8, February.
    3. Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July.
    4. 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.
    5. Koul, Hira L. & Surgailis, Donatas, 2001. "Asymptotics of empirical processes of long memory moving averages with infinite variance," Stochastic Processes and their Applications, Elsevier, vol. 91(2), pages 309-336, February.
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