A simulation study of ridge regression method with autocorrelated errors

Abstract EN

The objective of this paper is to discuss the case of the general linear model that suffered from both problems autocorrelation ( AR(1) and AR(2) ) and multicollinearity, we used simulation technique to create data containing these problems at the same time , and we used the two stages least squares procedure (2SLS) to deal with problem of autocorrelation , and ridge regression (RR) to deal with the problem of multicollinearity of the data that had originally treated in consideration of autocorrelation.

Moreover we used the evaluation methods as a base for the process of evaluation and comparison.

Throughout the simulation experiment results domain we concluded that dealing with autocorrelation from data that suffered from multicollinearity, multicollinearity increases when the error term follows first or second order autoregressive scheme.

Whereas, multicollinearity decreases if the model has a few explanatory variables .Among the types of ridge regression method , if we take the MSE as a criteria of comparison we find that ordinary ridge regression is the best when the sample size is too large, otherwise , generalized ridge regression is the best one.