Effect of Government Debt on the Growth of Nigerian Economy: An Econometric Approach

Abstract

This study examined the effect of government debt on the growth of the Nigerian economy. The study was specifically meant to access the extent to which external debt, domestic debt and exchange rate relate with the growth of the Nigerian economy. To achieve these objectives, an ex-post facto research design was adopted for the study. Time series data was collected from the CBN Statistical Bulletin and the National Bureau of statistics for the period 1990 to 2021 using the desk survey approach. The data were analyzed using the ordinary least square multiple regression statistical technique and the correlation matrix. Results from the analysis revealed that external debt had a negative but significant effect on the growth of the Nigerian economy. Also, the study showed that exchange rate and domestic debt had positive and significant effect on economic growth in Nigeria. Based on these findings, it was recommended that funding through government external borrowing should be minimized and allocated for funding long term viable capital project. Also it will boost the level of the economy into growth and generate sufficient returns required to service the debt. Also, borrowed funds should be invested by government in providing an enabling environment to promote the export base of the country and reduce the over reliance on importation of consumables and industrial raw materials.

Explainable ensemble technique for enhancing credit risk prediction

“…ML methods have gained popularity in credit risk prediction due to their capacity to handle large amounts of data and capture nonlinear relationships between variables. Decision trees, random forests, support vector machines, and neural networks are some of the commonly used algorithms in credit risk prediction [1], [9], [10]. Although these models have demonstrated success in improving the accuracy of credit risk prediction, their complexity and lack of interpretability present challenges in explaining model outputs to stakeholders.…”