Business Applications of Emulative Neural Networks
International Journal of BusinessPublished 22 September 2005
Yochanan Shachmurove
Citations10
SJR quartileQ4
SJR score0.19
SNIP0.19
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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 nonlinear 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. JEL: C3, C32, C45, C5, C63, F3, G15.
Keywords
Computer ScienceDecision SciencesEconomics, Econometrics and Finance
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