Modeling And Forecasting Africa's GDP With Time Series Models

Forecasting economic growth for developing countries is a problematic task,peculiarly because of particularities they face.The main instrument used in this study for the prediction of the value of an economic variable is Time series analysis,which exploits historical data to describe the time variation by using the proper model.In this thesis,the stochastic mechanism has been modeled using the Box-Jenkins methodology and based on its history,we predicted the future of GDP(Gross Domestic Product)series.The Box-Jenkins technique consists of a four-step iterative procedure,which is:model identification,model fitting,model diagnostics and forecasting.In view of the fact that the GDP is a non-stationary series,the first difference and the second difference was performed to make it stationary,and the Augmented Dickey-Fuller test was carried out to confirm the stationarity of the differenced series.The model identification process yielded a random walk model for the GDP series.We applied ARIMA models to get empirical results and we found that the models obtained are suitable for forecasting the economic output of Africa.As a final point,the adequate models were used for each of Africa's 20 largest economies to forecast future time series values.Based on the estimation results,we concluded that from 1990 looking forward to 2030,there will be an increasing GDP growth where the average speed of the economy of Africa will be of 5.52%,and GDP could achieve $2185.21 billion to $10186.18 billion.After the model was fit,the Theil's Inequality Coefficient was used to analyze the forecast accuracy.