Asymptotic normality of regression estimators with long memory errors

Liudas Giraitis (), Hira L. Koul and Donatas Surgailis

Statistics & Probability Letters, 1996, vol. 29, issue 4, 317-335

Abstract: This paper discusses asymptotic normality of certain classes of M- and R-estimators of the slope parameter vector in linear regression models with long memory moving average errors, extending recent results of Koul (1992) and Koul and Mukherjee (1993). Like in the case of the long memory Gaussian errors, it is observed that all these estimators are asymptotically equivalent to the least squares estimator, a fact that is in sharp contrast with the i.i.d. errors case.

Keywords: M-; R-estimators Appell polynomials Weighted empiricals (search for similar items in EconPapers)
Date: 1996
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Citations: View citations in EconPapers (30)

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