Forecasting a Mix of Temporal and Non-Temporal Economic Variables with a Mixture-of-Experts Neural Network

Abstract

This study investigates a versatile forecasting technique using an integrated system of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in a mixture-of-experts architecture to solve a general economic forecasting problem involving a mix of temporal and non-temporal variables. Using Klein Model I as a context and previous estimations from traditional methods as benchmarks, the study provides evidence on the effectiveness and efficiency of this integrated system. ANN helps overcome the imposition of assumptions on the behaviors of related variables, the specification of exact relationships, and the difficulty in nonlinear estimations of the economic model. GA helps overcome the sub-optimality of the tedious trial-and-error process in network building. The flexibility of the mixture-of experts network architecture offers many alternative configurations to capture the peculiarities of variables in context before aggregating intermediate estimations into the final result. The integrated system has shown its ability in processing effectively the mixture of economic variables, and producing efficient estimations and forecasts.