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

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  • Amiri, Arshia
  • Ventelou, Bruno

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.

Suggested Citation

  • Amiri, Arshia & Ventelou, Bruno, 2011. "Forecasting the role of public expenditure in economic growth Using DEA-neural network approach," MPRA Paper 33955, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:33955
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    References listed on IDEAS

    as
    1. Aschauer, David Alan, 1989. "Is public expenditure productive?," Journal of Monetary Economics, Elsevier, vol. 23(2), pages 177-200, March.
    2. Afriat, Sidney N, 1972. "Efficiency Estimation of Production Function," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 13(3), pages 568-598, October.
    3. Barro, Robert J, 1990. "Government Spending in a Simple Model of Endogenous Growth," Journal of Political Economy, University of Chicago Press, vol. 98(5), pages 103-126, October.
    4. Lynde, Catherine, 1992. "Private profit and public capital," Journal of Macroeconomics, Elsevier, vol. 14(1), pages 125-142.
    5. Devarajan, Shantayanan & Swaroop, Vinaya & Heng-fu, Zou, 1996. "The composition of public expenditure and economic growth," Journal of Monetary Economics, Elsevier, vol. 37(2-3), pages 313-344, April.
    6. Kneller, Richard & Bleaney, Michael F. & Gemmell, Norman, 1999. "Fiscal policy and growth: evidence from OECD countries," Journal of Public Economics, Elsevier, vol. 74(2), pages 171-190, November.
    7. Charnes, A. & Cooper, W. W., 1984. "The non-archimedean CCR ratio for efficiency analysis: A rejoinder to Boyd and Fare," European Journal of Operational Research, Elsevier, vol. 15(3), pages 333-334, March.
    8. Michael Bleaney & Norman Gemmell & Richard Kneller, 2001. "Testing the endogenous growth model: public expenditure, taxation, and growth over the long run," Canadian Journal of Economics, Canadian Economics Association, vol. 34(1), pages 36-57, February.
    9. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
    10. Hamilton, Clive & Turton, Hal, 2002. "Determinants of emissions growth in OECD countries," Energy Policy, Elsevier, vol. 30(1), pages 63-71, January.
    11. Fleissig, Adrian R. & Kastens, Terry & Terrell, Dek, 2000. "Evaluating the semi-nonparametric fourier, aim, and neural networks cost functions," Economics Letters, Elsevier, vol. 68(3), pages 235-244, September.
    12. Ventelou, Bruno & Bry, Xavier, 2006. "The role of public spending in economic growth: Envelopment methods," Journal of Policy Modeling, Elsevier, vol. 28(4), pages 403-413, May.
    13. Charnes, A. & Cooper, W. W. & Rhodes, E., 1979. "Measuring the efficiency of decision-making units," European Journal of Operational Research, Elsevier, vol. 3(4), pages 339-338, July.
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    More about this item

    Keywords

    DEA method; Economic growth; Public expenditure; Artificial neural network; OCDE countries;

    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

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