Asymptotics of M-estimators in non-linear regression with long memory designs
This paper derives the asymptotic distribution of a class of M-estimators in a family of non-linear regression models when the errors and the design variables are long memory moving averages. The class of estimators includes analogs of the least square, least absolute deviation and the Huber(c) estimators. A simulation study comparing the finite sample behaviour of the least absolute deviation and the least-square estimators is also included.
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Volume (Year): 61 (2003)
Issue (Month): 3 (February)
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- 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.
- 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.
- Baillie, Richard T & Bollerslev, Tim, 1994.
"The long memory of the forward premium,"
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Elsevier, vol. 13(5), pages 565-571, October.
- Baillie, R.T. & Bollerslev, T., 1993. "The Long Memory of the Foreward Premium," Papers 9203, Michigan State - Econometrics and Economic Theory.
- Baillie, Richard T., 1996. "Long memory processes and fractional integration in econometrics," Journal of Econometrics, Elsevier, vol. 73(1), pages 5-59, July. Full references (including those not matched with items on IDEAS)
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