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

Izmir Bakircay University Department of International Trade and Business; Economic Research Forum (ERF)

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)

Izmir Bakircay University Department of International Trade and Business ( email )

Gazi Mustafa Kemal Mahallesi
Kaynak Caddesi Seyrek Menemen
Izmir, 35660
Turkey

Economic Research Forum (ERF) ( email )

21 Al-Sad Al-Aaly St.
(P.O. Box: 12311)
Dokki, Cairo
Egypt

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