A simplified GLS estimator for autoregressive moving-average models
A simplified GLS estimator for autoregressive moving-average models
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DOI：
10.1080/135048598354915
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发表时间：
1998-04-01
影响因子：
1.6
通讯作者：
Power, S
中科院分区：
文献类型：
--
作者：
Choudhury, AH;Power, S
Koreisha和Pukkila（1990 a）提出了一种快速有效的单变量阿尔马时间序列模型的GLS估计方法，它比极大似然法更稳健，且具有相当的精度。这个新估计器的一个缺点是它需要使用Cholesky分解。本文的目的是提出一种替代的简化GLS估计，它可以实现与OLS子程序的重复应用。有限的蒙特卡罗研究表明，这种新的估计是一样有效的Koreisha和Pukkila。
Koreisha and Pukkila (1990a) have recently proposed a fast and efficient GLS estimator for the univariate ARMA time series model which appears to be far more robust than maximum likelihood methods and of comparable accuracy. The one drawback to this new estimator is that it requires use of the Cholesky decomposition. The purpose of this paper is to suggest an alternative simplified GLS estimator, which can be implemented with just repeated applications of an OLS subroutine. A limited Monte Carlo study establishes that this new estimator is just as efficient as that of Koreisha and Pukkila.