By Topic

Parameter estimation of ARMA models using a computationally efficient maximum likelihood technique

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Sarris, A.H. ; NBER Computer Research Center for Economics ; Eisner, M.

A method is presented for estimating the parameters of a fixed order autoregressive moving average model, based on maximization of an appropriate likelihood function. The resulting static optimization is accomplished with a modified Newton numerical algorithm. Under suitable initial conditions for the model, the gradient and hessian matrices of each iteration can be compted analytically. Because of the highly nonlinear character of the likelihood function, starting values of the algorithm are important. Extensions of the method to more complicated models are described. Some numerical examples illustrate the properties of the method.

Published in:

Decision and Control including the 12th Symposium on Adaptive Processes, 1973 IEEE Conference on

Date of Conference:

5-7 Dec. 1973