Asymptotics of M-estimators in non-linear regression with long memory designs

被引：7
|
作者：
Koul, HL
Baillie, RT
机构：
[1] Michigan State Univ, Dept Stat & Probabil, E Lansing, MI 48824 USA
[2] Michigan State Univ, Dept Econ, E Lansing, MI 48824 USA
来源：
STATISTICS & PROBABILITY LETTERS | 2003年 / 61卷 / 03期
关键词：
least absolute deviation estimators; root mean squared error; forward premiums;
D O I：
10.1016/S0167-7152(02)00354-1
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 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. (C) 2002 Elsevier Science B.V. All rights reserved.
引用
收藏
页码：237 / 252
页数：16
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