Ridge Estimation in Linear Models with Autocorrelated Errors
This article deals with multiple linear regression models in which the disturbances are autocorrelated and the explanatory variables are collinear. For these models we have derived the conditions for comparing ridge estimators based on combining the RLS and the auto collinear ridge (ACR) regression methods with the RLS estimator by means of their second-order moment matrices when the restrictions are not true. Monte Carlo simulations (1,000 replications) are also performed to show superiority of the proposed ACR estimators over the GLS estimator when compared in terms of TMSE and PR criteria, in particular and .