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

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).