Cascade Artificial Neural Networks for Modeling Economic Performance: A New Perspective

Ahmed R. Mohamed^1,

¹Economic Department, Modern Academy for Computer Science and Management Technology, Cairo, Egypt

Cite this paper:
Ahmed R. Mohamed. Cascade Artificial Neural Networks for Modeling Economic Performance: A New Perspective. International Journal of Econometrics and Financial Management. 2020; 8(2):46-60. doi: 10.12691/ijefm-8-2-2

Abstract

This paper discusses a new representation for efficiency frontier method through a proposed algorithm for augmented feed forward back propagation neural network models, to estimate the economic performance, and the effectiveness of macroeconomic policies in Egyptian economy, using a quarter time series data from 1990Q1 to 2019Q2. In this study I develop artificial neural network models - ANN - in line with the conditions of the Egyptian economy, by building an optimal efficiency frontier and then comparing the actual performance of the Egyptian economy with that limit, which includes the lowest possible variations for both inflation and output. As for the new contribution to this study, it is to calculate the optimal inflation rate and the optimal output level in the Egyptian economy through a model that combines the higher predictive power of feed forward neural network models, and the high explanatory power of a stationary or random walk stochastic models, in order to obtain the fitted values of the optimal output level, and the optimal inflation rate. It is clear from the results of the study, the extent of the essential congruence between the actual Egyptian economic performance during the study period and the economic performance index that was built through the new contribution of this study.

Keywords:
Artificial neural network models Efficiency frontier method New loss function Economic performance index

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References:

[1]	Cecchetti, S. and Krause, S., 2002. "Central Bank Structure Policy Efficiency, and Macroeconomic Performance: Exploring Empirical Relationships", The Federal Reserve Bank of St. Louis: 47-60.
[2]	Cecchetti, S. et al., 2006. "Has Monetary Policy Become More Efficient? A Cross Country Analysis", Economic Journal, 116(511): 408-433.
[3]	Rzhevskyy, A. et al., 2018. "Policy Rules and Economic Performance", Department of Economics, University of Houston. working papers 2018: 1-46.
[4]	Cecchetti, S. and Krause, S., 2001. "Financial Structure, Macroeconomic Stability and Monetary Policy", National Bureau Of Economic Research, working paper series No. 8354: 1-31.
[5]	Taylor, John B., 2017. "Rules versus Discretion: Assessing the Debate over the Conduct of Monetary Policy", NBER Working Paper No. 24149, April 27-28: 1-41.
[6]	Koop, G. et al., 2009. "On the evolution of monetary policy", Journal of Economic Dynamics and Control, working paper No. 33: 997-1017.
[7]	Olson, E. and Enders, W., 2012. "A Historical Analysis of the Taylor Curve", Journal of Money, Credit and Banking 44(7): 1285-1299.
[8]	Destefanis, S. et al., 2018. "On the Macroeconomic Performance of the Euro Area", Barcelona A Euro Area Business Cycle Network (EABCN) Conference, April 27-28: 1-45.
[9]	Mehrotra, K. et al., "Elements of Artificial Neural Networks", Bradford Books, 1st Edition: 1-345.
[10]	Stephenson, C., 2010. "Hebbian neural networks and the emergence of minds", International Journal of Forecasting 26(2): 1-32.
[11]	Gonzalez, S., 2000. "Neural Networks for Macroeconomic Forecasting: A Complementary Approach to Linear Regression Models", Bank of Canada - Department of Finance, working paper No. 7: 1-48.
[12]	Chen, J., 2005. "Neural Network Applications In Agricultural Economics", University of Kentucky, Doctoral Dissertations: 1-213.
[13]	Kuan, C. and Halbert, W.,1994. "Artificial Neural Networks: An Econometric Perspective", Econometric Reviews 13(1): 1-91.
[14]	Aaron, H. and Thomas, C., 2017. "Macroeconomic Indicator Forecasting with Deep Neural Networks", Federal Reserve Bank of Kansas City, RWP 17(11): 1-38.
[15]	Michael, C., 2016. "Neural Nets for Indirect Inference", Econometrics and Statistics Journal 4(1): 132-147.
[16]	Ayodele, A. et al., 2014. "Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction", Journal of Applied Mathematics, 2014(1): 1-12.
[17]	Kock, A. and Teräsvirta, T., 2014. "Forecasting Performances of Three Automated Modeling Techniques During the Economic Crisis 2007-2009", International Journal of Forecasting 30: 616-631.
[18]	Subramanian, A., 1997. "The Egyptian Stabilization Experience - An Analytical Retrospective", The International Monetary Fund working paper No. 97(105): 1-57.
[19]	Hans, L., 2009. "Economic Policy in Egypt: A Breakdown in Reform Resistance?", International Journal of Middle East Studies, 25(3): 407-421.