Open Access Open Access  Restricted Access Subscription or Fee Access

A general class of biased estimators in the presence of multicollinearity with autocorrelated errors

Shalini Chandra, Gargi Tyagi


It is a well known fact in regression analysis that multicollinearity and autocorrelated errors have adverse effects on least squares estimator. In this paper, the r 􀀀 (k; d) class estimator (O¨ zkale, 2012) which was developed to combat the problem of multicollinearity has been generalized to address autocorrelated errors simultaneously. Necessary and sufficient conditions for superiority of the proposed estimator over other competing estimators have been derived under mean squared error (MSE) matrix criterion. A simulation study has been done to evaluate the performance of the estimators.

Full Text: PDF

Regarding indexing issue:

We have provided the online access of all issues & papers to the all  indexing agencies (as given on our journal home web site). It’s depend on indexing agencies when, how and what manner they can index or not. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies. 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.