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

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.