Abstract
Developing countries like Nigeria have been hit hard by recent economic crises, and uncertainty is one of the characteristics that all prices and revenues share. A forecast of Nigerian economic growth was attempted using an artificial neural network (ANN) and statistical model to analyse the contribution of non-oil income tax generation to Nigerian economic development. While the primary objective is to develop and implement the proposed models capable of simulating a real non-oil income tax and evaluate their performances. The dataset (Corporate Income Tax) from 2015–2020 was obtained from the National Bureau of Statistics of Nigeria (NBSN). Three training algorithms for ANN were adopted, such as conjugate gradient back-propagation with Fletcher-Reeves restarts, Bayesian regularisation, and gradient descent with an adaptive learning rate, whereas in the statistical part, multiple linear regressions were applied. Comparing all the models revealed that the Bayesian regularisation produced more accurate results than the other models.
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