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Using Neural Nets to Forecast the Unemployment Rate

A Promising Application Of An Emerging Quantitative Method

By Rolando F. Peláez

Rolando F. Pelaez is professor of finance at the University of Houston-Downtown. He holds a Ph.D. degree in economics from the University of Houston.

The paper identifies leading indicators of the unemployment rate. Forecasts of the unemployment rate are obtained with an econometric model, and with an artificial neural network. Both model-based forecasts outperform forecasts from the Survey of Professional Forecasters. This is important because the unemployment rate forecast from the Survey of Professional Forecasters has outperformed other forecasts based on time-series models to the point that some observers view it as a proxy for a full-information forecast.

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