A Simulation Study of Ridge Regression Estimators with Autocorrelated Errors

In this paper we run a large number of simulations to study the effects of collinearity and autocorrelated disturbances in the performance of several Ridge Regression estimators. The results suggest that with a fair amount of multicollinearity and of autocorrelation the Ridge Regression estimators which take the autocorrelation into account can perform better than the other methods. Also if the error term is only moderately autocorrelated; then the performance of the Ridge Regression estimators built upon ignoring the autocorrelation can outperform the other estimators.