@inbook{c055d37da37742c6a2f08c4b0e57e69c,
title = "CO-EVOLVING NEURAL NETWORKS WITH EVOLUTIONARY STRATEGIES: A NEW APPLICATION TO DIVISIA MONEY",
abstract = "This work applies state-of-the-art artificial intelligence forecasting methods to provide new evidence of the comparative performance of statistically weighted Divisia indices vis-{\`a}-vis their simple sum counterparts in a simple inflation forecasting experiment. We develop a new approach that uses co-evolution (using neural networks and evolutionary strategies) as a predictive tool. This approach is simple to implement yet produces results that outperform stand-alone neural network predictions. Results suggest that superior tracking of inflation is possible for models that employ a Divisia M2 measure of money that has been adjusted to incorporate a learning mechanism to allow individuals to gradually alter their perceptions of the increased productivity of money. Divisia measures of money outperform their simple sum counterparts as macroeconomic indicators.",
author = "Binner, {Jane M.} and Graham Kendall and Alicia Gazely",
year = "2004",
month = dec,
day = "1",
doi = "10.1016/S0731-9053(04)19005-1",
language = "English",
isbn = "0762311509",
series = "Advances in Econometrics",
pages = "127--143",
editor = "Jane Binner and Graham Kendall and Shu-Heng Chen",
booktitle = "Applications of Artificial Intelligence in Finance and Economics",
}