On some simple, autoregression-based estimation and identification techniques for ARMA models
On some simple, autoregression-based estimation and identification techniques for ARMA models
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关于 ARMA 模型的一些简单的、基于自回归的估计和识别技术
DOI：
10.1093/biomet/84.3.685
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发表时间：
1997
期刊：
影响因子：
2.7
通讯作者：
Victoria Zinde
中科院分区：
文献类型：
--
作者：
John W. Galbraith;Victoria Zinde
总结我们研究了一般阿尔马模型的简单估计和相应的识别方法。估计和识别都是基于一个矩阵，该矩阵由对感兴趣的过程的自回归近似的系数形成。我们表明，这个矩阵的零行列式是必要的和充分的自回归和移动平均滞后多项式中存在一个共同的因素，因此在模型中的冗余参数。仿真结果表明，模型降阶的检验统计量的经验有限样本分布与其渐近分布之间的密切匹配。
SUMMARY We examine simple estimators for general ARMA models and a corresponding identification method. Both estimation and identification are based on a matrix formed from the coefficients of an autoregressive approximation to the process of interest. We show that a zero determinant of this matrix is necessary and sufficient for the existence of a common factor in autoregressive and moving average lag polynomials, and therefore for redundant parameters in the model. Simulation results suggest a close match between the empirical finite-sample distribution of the test statistic for model order reduction and its asymptotic distribution.