Amiri, Arshia and Ventelou, Bruno (2011): Forecasting the role of public expenditure in economic growth Using DEA-neural network approach.
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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.
|Item Type:||MPRA Paper|
|Original Title:||Forecasting the role of public expenditure in economic growth Using DEA-neural network approach|
|Keywords:||DEA method; Economic growth; Public expenditure; Artificial neural network; OCDE countries|
|Subjects:||C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods; Simulation Methods
G - Financial Economics > G1 - General Financial Markets > G18 - Government Policy and Regulation
G - Financial Economics > G3 - Corporate Finance and Governance > G38 - Government Policy and Regulation
H - Public Economics > H5 - National Government Expenditures and Related Policies
|Depositing User:||Arshia Amiri|
|Date Deposited:||08. Oct 2011 14:31|
|Last Modified:||15. Feb 2013 17:20|
Afriat, S.N. (1972) Efficiency estimation of production function. International Economic Review 13: 568–598.
Aschauer, D.A. (1988) Governement spending and the falling rate of profit. Economic Perspectives (12).
Aschauer, D.A. (1989) Is public expenditure productive? Journal of Monetary Economics 23: 177–200.
Athanassopoulos, A.D. & Curram, S.P. (1996) A comparison of data envelopment analysis and artificial neural networks as tool for assessing the efficiency of decision making units. Journal of the Operational Research Society 47(8): 1000–1016.
Barro, R. J. (1990) Government spending in a simple model of endogenous growth. Journal of Political Economy 98: 103–125.
Banker, R.D., Charnes, A. & Cooper, W. (1984) Some model for estimating technical and scale inefficiencies in data envelopment analysis. Management Science 30: 1078–1092.
Bleaney, M., Gemmel, N., & Kneller, R. (2001) Testing endogenous growth model: public expenditure, taxation and growth over the long run. Canadian Journal of Economics 34(1).
Charnes, A. & Cooper, W. (1984) The non-Archimedean CCR ratio for efficiency analysis: a rejoinder to Boyd and Fare, European Journal of operational Research 15: 333–334.
Charnes, A., Cooper, W. & Rhodes S.E. (1978) Measuring efficiency of decision-making units. European Journal of operational Research 2(6).
Charnes, A., Cooper, W. & Rhodes S.E. (1979) Measuring efficiency of decision-making units. European Journal of operational Research 3: 339.
Devarajan, S., Swaroop, V. & Zou, H.F. (1996) The composition of public expenditure and economic growth. Journal of Monetary Economics 37: 313–334.
Costa, A. & Markellos, R.N. (1997) Evaluating public transport efficiency with neural network models. Transportation Research C, 5(5): 301–312.
Farrell, M.J. (1957) The measurement of productive efficiency. Journal of Royal Statistical Society, A CXX, Part 3: 253–290.
Fleissig, A.R., Kastens, T. & Terrell, D. (2000) Evaluating the seminonparametric fourier, aim, and neural networks cost functions. Economics Letters 68(3): 235–244.
Hamiltona, C. & Turton, H. (2002) Determinants of emissions growth in OECD countries. Energy Policy 30: 63–71.
Hecht Nielsen, R. (1990) Neural computing Reading. MA: Addison Wesley, p. 124–133
Kneller, R., Bleaney, M.F. & Gemmell, N. (1999) Fiscal policy and growth: evidence from OECD countries. Journal of Public Economics 74: 171–190.
Liang, L. & Wu, D. (2005) An application of pattern recognition on scoring Chinese corporations financial conditions based on backpropagation neural network. Computers and Operations Research 32(5).
Lynde, C. (1992) Private profit and public capital. Journal of Macroeconomics 14(1).
Pendharkar, P.C. & Rodger, J.A. (2003) Technical efficiency-based selection of learning cases to improve forecasting accuracy of neural networks under monotonicity assumption. Decision Support Systems 36(1): 117–136.
Poli, I. & Jones, R.D. (1994) A neural net model for prediction. Journal of the American Statistical Association 89: 117–121.
Shavlik, J.W., Mooney, R.J. & Towell, G.G. (1991) Symbolic and neural learning algorithms: An experimental comparison. Machine Learning 6: 111–143.
Ventelou, B. & Bry, X. (2006) The role of public spending in economic growth: Envelopment methods. Journal of Policy Modeling 28: 403–413.
Wu, D., Yang, Z. & Liang, L. (2006) Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank. Expert Systems with Applications 31: 108–115.