ANALISIS REGRESI RIDGE DUA TAHAP UNTUK PERMASALAHAN MULTIKOLINEARITAS

Regression analysis is a statistical analysis that used to perform model relationship between dependent variable and independent variable. One of the assumption in classical regression analysis is there is no multicollinearity problem. If there is multicollinearity in the regression model, it could cause the results of model that using the method of Least Squares estimator becomes invalid. Over the years, there are a lot of variety modern regression analysis. And one of the modern regression analysis that can overcome the multicollinearity problem is the Ridge regression analysis. Ridge regression analysis was first introduced by A.E Hoerl and Kennard in 1970. Two Stages Ridge regression analysis recently introduced by Hussein Eledum and Mostafa Zahri in 2013. Two Stages Ridge Regression analysis method is a combination of Two Stage Least Squares and Ordinary Ridge Regression. In this paper, two stages of Ridge regression analysis was applied to the analysis of the factors that affect the amount of money circulating in the U.S. to obtain the model that free from multicollinearity problem.