Business Applications of Emulative Neural Networks

Abstract

This paper surveys research on Emulative Neural Network (ENN) models as economic forecasters. ENNs are statistical methods that seek to mimic neural processing. They serve as trainable analytical tools that "learn" autonomously. ENNs are ideal for finding non-linear relationships and predicting seemingly unrecognized and unstructured behavioral phenomena. As computing power rapidly progresses, these models are increasingly desirable for economists who recognize that people act in dynamic ways with rational expectations. Unlike traditional regressions, ENNs work well with incomplete data and do not require normal distribution assumptions. ENNs can eliminate substantial uncertainty in forecasting, but never enough to completely overcome indeterminacy.

Keywords: Emulative neural networks, Dynamic interrelations, Nonlinear forecasting, Perceptron learning process, Multi-layer perceptron model, Learning, Observational indeterminability, Indeterminacies

JEL Classification: C3, C32, C45, C5, C63, F3, G15