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Artificial Neural Network for Modeling the Economic Performance: A New Perspective

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  • Ahmed Ramzy Mohamed

    (Modern Academy for Computer Science and Management Technology)

Abstract

This paper discusses a new representation for the efficiency frontier method through a proposed algorithm for augmented feed forward back propagation neural network models, in order to estimate the economic performance, and the effectiveness of macroeconomic policies in Egyptian economy, by using a quarter time series data from 1990Q1 to 2019Q2. In this study I developed artificial neural network models—ANN—corresponding 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 of this study, it is designated to calculate the optimal inflation rate and the optimal output level in the Egyptian economy through a model, which 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, in addition to 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 via the new contribution of this study.

Suggested Citation

  • Ahmed Ramzy Mohamed, 2022. "Artificial Neural Network for Modeling the Economic Performance: A New Perspective," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(3), pages 555-575, September.
    • Handle: RePEc:spr:jqecon:v:20:y:2022:i:3:d:10.1007_s40953-022-00297-9
    DOI: 10.1007/s40953-022-00297-9
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    References listed on IDEAS

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