Forecasting Economic Data with Neural Networks

Authors:
Farzan Aminian;E. Dante Suarez;Mehran Aminian;Daniel T. Walz
Affiliations:
Department of Engineering Science, Trinity University, San Antonio, USA 78212;Department of Business Administration, Trinity University, San Antonio, USA 78212;Department of Engineering, St. Mary's University, San Antonio, USA 78228;Department of Business Administration, Trinity University, San Antonio, USA 78212
Venue:
Computational Economics
Year:
2006

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Abstract

Studies in recent years have attempted to forecast macroeconomic phenomena with neural networks reporting mixed results. This work represents an investigation of this problem using U.S. Real Gross Domestic Production and Industrial Production as case studies. This work is based on a coefficient of determination which accurately measures the ability of linear or nonlinear models to forecast economic data. The significance of our work is twofold: (1) It confirms recent work that neural networks significantly outperform linear regression due to nonlinearities inherent in the data sets, and (2) it provides a systematic approach that guarantees to find the maximum correlation between input(s) and output of interest.