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

Linyuan Li

Stochastic Processes and their Applications, 2003, vol. 105, issue 2, 257-269

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.

Keywords: Long-range; dependence; Multiple; linear; model; Weighted; empirical; Asymptotic; normality (search for similar items in EconPapers)
Date: 2003
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