The Performance of the Kernel Regression Model for Assessing the Impact of Money Supply on Industrial Growth in Nigeria

Abstract

This study examined the performance of the kernel model over the linear regression model for a real-life application in Nigeria. The linear regression and kernel regression model was used to assess the impact of the volume of money supply in Nigeria on industrial growth in Nigeria. The source of data for this study was the secondary source of data collection. Findings showed that there exist a weak positive coefficient of determination measure between volume of money supply and industrial growth which implies that the volume of money weakly explains the total amount of variation in industrial growth using the linear regression model while the kernel model found a strong positive coefficient of determination value which implies that the kernel model was adequate and far better than the linear model for estimating industrial growth in Nigeria. Also, it was found that volume of the money supply does not impact significantly on industrial growth in Nigeria using the linear model while it was found that volume of money impacts significantly on industrial growth using the kernel model. Further findings showed that the residual standard error value for the smoothed model is relatively more efficient than that of the linear model which was attributed to the performance of the kernel regression model.