Open Access Open Access  Restricted Access Subscription Access

Model with AR(1) Error: A Comparative Simulation Study

M. I. Alheety, B. M Golam Kibria


The performance of shrinkage ridge estimators in the linear regression model has been studied to reduce the effect of multicollinearity on the estimation of the linear regression parameters. Trenkler (1984) proposed a ridge estimator in the linear regression model when the assumption of uncorrelatedness is not satisfied. Since there is no attempt to study the recent types of estimated ridge parameter when the assumption of uncorrelatedness is not satisfied, this paper tries to show the performance of some ridge estimators in the linear regression model with correlated error based on the minimum mean squared error (MSE) criterion. A simulation study and a numerical example have been made to evaluate the performance of these estimators of ridge parameter k. The simulation study suggests that some ridge estimators are promising and can be recommenced for the practitioners.


Linear Model; Multicollinearity; MSE; Ridge Regression; Simulation Study.

Full Text:


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.