Modular Perspective for Population and Gross National Income Time Series Prediction Using a Neural Network Model: A Case Study of OECD Member Countries

The evolution of some social and economic factors has contributed to the changes experienced by global components. Additionally, the constant changes in significant indicators represent new challenges and opportunities for governments. So, in this work we propose a method to predict population and gross national income time series using a modular perspective in a neural network model. Simulation results show the advantages of the proposed method for prediction of time series. Also, the use of neural networks allows making an analysis of population growth and determination of causal relationships that may exist between demographic and economic factors for some specific location or cluster in a particular period.