Affordable Access

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

Authors
Publication Date
Keywords
  • C53 - Forecasting And Prediction Methods
  • Simulation Methods
  • G18 - Government Policy And Regulation
  • G38 - Government Policy And Regulation
  • H5 - National Government Expenditures And Related Policies
Disciplines
  • Computer Science
  • Economics

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.

There are no comments yet on this publication. Be the first to share your thoughts.