Asymptotic Normality of the Whittle Estimator in Linear Regression Models with Long Memory Errors

被引：0
|
作者：
Hira L. Koul
Donatas Surgailis
机构：
[1] Michigan State University,Department of Statistics & Probability
[2] Informatics and Siauliai University,Vilnius Institute of Mathematics
来源：
Statistical Inference for Stochastic Processes | 2000年 / 3卷 / 1-2期
关键词：
Moving average long memory errors; i.i.d. and long memory designs; unbounded spectral density;
D O I：
10.1023/A:1009999607588
中图分类号：
学科分类号：
摘要：
This paper establishes the asymptotic normality of the Whittle estimator of the unknown dependence parameters in a linear regression model with long memory moving average errors. The design variables are taken to be deterministic or random. In the latter case they are assumed to have a moving average representation that includes both short and long memory. In all cases, it is observed that the rate of consistency of the regression parameter estimator has an effect on the asymptotic normality of the Whittle estimator.
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收藏
页码：129 / 147
页数：18
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