This paper surveys the significance of recent work on emulative neural networks (ENNs) by researchers across many disciplines in the light of issues of indeterminacy. Financial and economic forecasters have witnessed the recent development of a number of new forecasting models. Traditionally, popular forecasting techniques include regression analysis, time-series analysis, moving averages and smoothing methods, and numerous judgmental methods. However, all of these have the same drawback insofar as they require assumptions about the form of population distribution. Regression models, for example, assume that the underlying population is normally distributed.
ENNs are members of a family of statistical techniques, as are flexible nonlinear regression models, discriminant models, data reduction models, and nonlinear dynamic systems. They are trainable analytic tools that attempt to mimic information processing patterns in the brain. Because they do not necessarily require assumptions about population distribution, economists, mathematicians and statisticians are increasingly using ENNs for data analysis.