Analysis of leading economic indicator data and gross domestic product data using neural network methods

Edward Tirados, John Jenq

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this report, Leading Economic Indicator (LEI) data and Gross Domestic Product (GDP) data have been analyzed to determine if changes in the ten indicators can be used to predict changes in GDP. Three neural network methods and one statistical method were used to complete the analysis. For this project, the intent was to use multiple regression and backpropagation to develop correlations in which LEI values are used to predict the GDP change in the following quarter. Alternatively, Kohonen's self-organizing map and hierarchical clustering were used to group months of LEI data into clusters to determine if months in a cluster (and thus months with similar LEI values) also have similar changes in GDP.

Original languageEnglish
Title of host publicationIMETI 2008 - International Multi-Conference on Engineering and Technological Innovation, Proceedings
Pages164-169
Number of pages6
Publication statusPublished - 1 Dec 2008
EventInternational Multi-Conference on Engineering and Technological Innovation, IMETI 2008 - Orlando, FL, United States
Duration: 29 Jun 20082 Jul 2008

Publication series

NameIMETI 2008 - International Multi-Conference on Engineering and Technological Innovation, Proceedings
Volume2

Other

OtherInternational Multi-Conference on Engineering and Technological Innovation, IMETI 2008
CountryUnited States
CityOrlando, FL
Period29/06/082/07/08

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Tirados, E., & Jenq, J. (2008). Analysis of leading economic indicator data and gross domestic product data using neural network methods. In IMETI 2008 - International Multi-Conference on Engineering and Technological Innovation, Proceedings (pp. 164-169). (IMETI 2008 - International Multi-Conference on Engineering and Technological Innovation, Proceedings; Vol. 2).