Regression techniques in the presence of multicollinearity and autocorrelation phenomena: Monte Carlo approach

摘要

Multicollinearity and Autocorrelation are two very common problems in regression analysis. As its well-known, the presence of some degrees of multicollinearity results in estimation instability and model mis-specification while the presence of serial correlated errors lead to underestimation of the variance of parameter estimates and inefficient prediction. These two conditions have adverse effects on estimation and prediction; therefore, a wide range of tests have been developed to reduce their impact. Invariably, the multicollinearity and autocorrelation problems are dealt with separately in most studies. Thus, this study explored the predictive ability of the proposed GLS-Ridge regression on multicollinearity and autocorrelation problems simultaneously, using simulated dataset. Data used for the study was the data simulated using Monte Carlo. The research work revealed that the GLS-R regression technique has a better predictive ability in the presence of autocorrelation and multicollinearity, hence it is preferred than the other three techniques.