Cart (Loading....) | Create Account
Close category search window
 

An Empirical Test of New Forecasting Methods Derived from a Theory of Intelligence: The Prediction of Conflict in Latin America

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)

The "compromise" method is a new computer-based forecasting tool, available within the conversational CS package on the MIT Multics. Like regression (least squares) or new forms of Box-Jenkins methods, it estimates the parameters of a multivariate dynamic model and may be used for causal analysis or policy impact analysis. Unlike those maximum-likelihood methods, it does not assume that errors are "white noise," random and normal. It follows the newer robust philosophy of trying to minimize estimation errors on the assumption that noise will be inextricably dirty. In the case of "strong" dynamic models¿models which predict that changes in present variable values lead to comparable changes in future variable values it may reduce parameter errors by an order of magnitude. Forecasting errors will also be reduced, although the degree of reduction depends on how much randomness exists in the process. When we used the compromise method according to the new "bias" procedure, in order to reestimate the J-5 model (a nonlinear multiequation model used by the Department of Defense in long-range forecasting), forecasting errors were reduced by between 0 and 45 percent (with a median of about 20 percent) across different variables, as compared with regression. With simultaneous-equation econometric models, it has reduced them by 50 percent. The procedure has been documented for use by nonprogrammers [1]; it incorporates a new quasi-Newtonian method which can handle many parameters.

Published in:

Systems, Man and Cybernetics, IEEE Transactions on  (Volume:8 ,  Issue: 9 )

Date of Publication:

Sept. 1978

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.