Can a Decision Tree Forecast Real Economic Growth from Relative Depth of Financial Sector In Nigeria?
We employed a decision tree statistical learning method which is lately gaining wide usage in the field of econometrics to establish the relationships between real gross domestic products growth rate and financial depth indicators such as stock market turnover ratio, credit to private sector (CPS) and broad money supply (M2) relative to gross domestic product (GDP) in Nigeria between 1981 to 2016. The data was divided into training and test datasets. The former was used to train the decision tree while the later was used to test the performance of the fitted decision tree model. Recursive binary splitting produced a fitted tree with nine nodes (leaves). This tree was pruned using cost complexity pruning procedure which uses a tuning parameter to control the tradeoff between the tree complexity and overfitting the data. Pruning produced a tree with four terminal nodes and improved predictability in terms of lower model MSE on test dataset and interpretability. Bagging and Random Forest procedure were employed to further improve the performance of the model by aggregating bootstrapped training samples in order to reduce the variance. These resulted in lower model MSEs on the test dataset. The regression tree model reveals that stock market turnover ratio and broad money supply relative to GDP are the most important financial depth measures in the real economic growth model. However, real GDP growth rate rises with stock market turnover ratio but dips with values of broad money supply relative to GDP between 10 per cent and 15 per cent. The real GDP growth rate stability threshold for stock market turnover ratio and broad money supply (M2) relative to gross domestic product are 10 per cent and 20 per cent respectively. R programming language was used throughout the paper.
