eprintid: 1627
rev_number: 13
eprint_status: archive
userid: 6
dir: disk0/00/00/16/27
datestamp: 2013-07-08 13:33:12
lastmod: 2014-01-24 14:25:00
status_changed: 2013-07-08 13:33:12
type: monograph
metadata_visibility: show
creators_name: Giovanis, Eleftherios
creators_id: eleftherios.giovanis@imtlucca.it
title: ARIMA and Neural Networks: An Application to the Real GNP Growth Rate and the Unemployment Rate of U.S.A.
ispublished: pub
subjects: E151
subjects: HA
subjects: HD
divisions: EIC
full_text_status: none
monograph_type: working_paper
keywords: Keywords: ARIMA, Radial basis function, Multilayer perceptron, Generalized regression neural networks, stationarity, unit root  -  JEL Classification: C22, C32, C45, C53
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. 
date: 2009-03
date_type: published
number:  
pages: 18
id_number: 10.2139/ssrn.1368675
institution: IMT Institute for Advanced Studies Lucca
official_url: http://ssrn.com/abstract=1368675
citation:   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