Comparing Ridge Regression and Principal Components Regression by Monte Carlo Simulation Based on MSE

Abstract

In multiple regression model, regression variables are usually assumed to be independent from each other. When this assumption is not established, the model would be inappropriate and therefore the results might be incorrect. So, biased regression methods are applied. Ridge regression and principal components regression are two methods of biased regression methods. In this paper, Monte Carlo simulation tests were used for estimating coefficients of ridge and principal components regression. These two methods were compared using minimum squared error (MSE).