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

18 Pages Posted: 26 Mar 2009

See all articles by Eleftherios Giovanis

Eleftherios Giovanis

Manchester Metropolitan University-Department of Economics, Policy and International Business; Nazilli Faculty of Economics and Administrative Sciences

Date Written: March 26, 2009

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

Suggested Citation

Giovanis, Eleftherios, ARIMA and Neural Networks: An Application to the Real GNP Growth Rate and the Unemployment Rate of U.S.A. (March 26, 2009). Available at SSRN: https://ssrn.com/abstract=1368675 or http://dx.doi.org/10.2139/ssrn.1368675

Eleftherios Giovanis (Contact Author)

Manchester Metropolitan University-Department of Economics, Policy and International Business ( email )

Business School
All Saints Campus
Manchester, M15 6BH
United Kingdom

Nazilli Faculty of Economics and Administrative Sciences ( email )

Nazilli IIBF
Sumer Kampusu
Aydin, Nazilli 09800
Turkey

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