A New Iterative Algorithm for Estimating Parameters and Orders of Multiple-Input Single-Output Time Series Models

Jacques Sabiti Kiseta, Emilie Epeka Mbambe, Angèle Yule Sotazo, Roger Akumoso Liendi
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引用次数: 1

Abstract

In this paper, we propose a new iterative algorithm for estimating the parameters and orders of a multiple-input single-output (MISO) time series model. This algorithm is based on a method suggested by Hannan and Rissanen (1982) for estimating an ARMA model. The key is the use of pseudo-linear regression techniques to derive the iterative nonlinear least-squares estimators by using the Gauss-Newton algorithm. Simulation results are presented to compare the new algorithm with the exact maximum likelihood method (EML) and the generalized least squares (GLS) method proposed by Sabiti (1997).
一种新的多输入单输出时间序列模型参数和阶数估计迭代算法
本文提出了一种新的多输入单输出(MISO)时间序列模型参数和阶数估计的迭代算法。该算法基于Hannan和Rissanen(1982)提出的估计ARMA模型的方法。关键是利用伪线性回归技术,利用高斯-牛顿算法推导迭代非线性最小二乘估计量。仿真结果与Sabiti(1997)提出的精确极大似然法(EML)和广义最小二乘(GLS)方法进行了比较。
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