Forecasting the role of public expenditure in economic growth Using DEA-neural network approach

Arshia Amiri and Bruno Ventelou

MPRA Paper from University Library of Munich, Germany

Abstract: This paper integrates data envelopment analysis (DEA) and artificial neural networks (ANN) to forecast the role of public expenditure in economic growth in OCDE countries. The results show that this approach is a powerful and appropriate method to forecast this role. DEA method allows us to develop a neutral evaluation, unbiased a priori by any type of criteria, of the proportions in which the goal of productive spending is pursued, for any expenditure. Then we apply ANN to forecast economic growth by using input data taken at frontier. At the end of the DEA-ANN chain, prediction-power tests appear positive: best structures of multiple hidden layers indicate more ability to forecast according to best structures of single hidden layer but the difference between those is not much.

Keywords: DEA method; Economic growth; Public expenditure; Artificial neural network; OCDE countries (search for similar items in EconPapers)
JEL-codes: C53 G18 G38 H5 (search for similar items in EconPapers)
Date: 2011-09-13
New Economics Papers: this item is included in nep-eff and nep-for
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