Assessing the Performance of Ordinary Least Square and Kernel Regression

The assessment of Ordinary Least Squares (OLS) and kernel regression on their predictive performance was studied. We used simulated data to assess the performance of estimators using small and large sample. However, the mean square error (MSE) and root mean square error (RMSE) was used to find out the most efficient among the estimated models. The results show that, when the ordinary least square is more efficient than the kernel regression due to having the least MSE and RMSE in both distributions. Whereas for the ordinary least square and the kernel regression have the same performance for normal distributed data while for lognormal, the result also shows that the kernel regression perform better than the ordinary least square. Finally, when, the kernel regression is more efficient than the ordinary least square for having the least MSE and RMSE in both distributions. The overall results show that the kernel regression estimate is more efficient than the ordinary least square (OLS) estimate