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

Giovanis, Eleftherios ARIMA and Neural Networks: An Application to the Real GNP Growth Rate and the Unemployment Rate of U.S.A. Working Paper # /2009

Full text not available from this repository.

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.

Item Type:	Working Paper (Working Paper)
Identification Number:	10.2139/ssrn.1368675
Uncontrolled Keywords:	Keywords: ARIMA, Radial basis function, Multilayer perceptron, Generalized regression neural networks, stationarity, unit root - JEL Classification: C22, C32, C45, C53
Subjects:	E History America > E151 United States (General) H Social Sciences > HA Statistics H Social Sciences > HD Industries. Land use. Labor
Research Area:	Economics and Institutional Change
Depositing User:	Ms T. Iannizzi
Date Deposited:	08 Jul 2013 13:33
Last Modified:	24 Jan 2014 14:25
URI:	http://eprints.imtlucca.it/id/eprint/1627

Actions (login required)

Edit Item