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

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.
Year of publication:
2003
Authors: Li, Linyuan
Published in:
Stochastic Processes and their Applications. - Elsevier, ISSN 0304-4149. - Vol. 105.2003, 2, p. 257-269
Publisher: Elsevier
Keywords: Long-range dependence Multiple linear model Weighted empirical Asymptotic normality
Type of publication: Article
Source:
Persistent link: https://www.econbiz.de/10008872620
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