Forecasting the role of public expenditure in economic growth Using DEA-neural network approach
AbstractThis 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.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 33955.
Date of creation: 13 Sep 2011
Date of revision:
DEA method; Economic growth; Public expenditure; Artificial neural network; OCDE countries;
Find related papers by JEL classification:
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
- G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation
- H5 - Public Economics - - National Government Expenditures and Related Policies
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-10-15 (All new papers)
- NEP-EFF-2011-10-15 (Efficiency & Productivity)
- NEP-FOR-2011-10-15 (Forecasting)
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