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

被引：7
|
作者：
Li, LY [1 ]
机构：
[1] Univ New Hampshire, Dept Math & Stat, Durham, NH 03824 USA
来源：
STOCHASTIC PROCESSES AND THEIR APPLICATIONS | 2003年 / 105卷 / 02期
关键词：
long-range dependence; multiple linear model; weighted empirical; asymptotic normality; LINEAR-REGRESSION; EMPIRICAL PROCESS; ASYMPTOTIC-EXPANSION; TIME-SERIES; ERRORS;
D O I：
10.1016/S0304-4149(02)00266-1
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
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. (C) 2003 Elsevier Science B.V. All rights reserved.
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页码：257 / 269
页数：13
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