Analysis of Leading Economic Indicator Data and Gross Domestic Product Data using Neural Network Methods

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Conference Proceeding

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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.

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