ARIMA and Neural Networks: An Application to the Real GNP Growth Rate and the Unemployment Rate of U.S.A.

Abstract

This paper examines the estimation and forecasting performance of ARIMA models in comparison with some of the most popular and common models of neural networks. Specifically we provide the estimation results of AR-GRNN (Generalized regression neural networks) and the AR-RBF (Radial basis function). We show that neural networks models outperform the ARIMA forecasting. We found that the best model in the case of real US GNP is the AR-GRNN and for US unemployment rate is the AR-MLP.

Keywords: ARIMA, Radial basis function, Multilayer perceptron, Generalized regression neural networks, stationarity, unit root

JEL Classification: C22, C32, C45, C53