Liu Estimates and Influence Analysis in Regression Models with Stochastic Linear Restrictions and AR (1) Errors

Abstract

In the linear regression models with AR (1) error structure when collinearity exists, stochastic linear restrictions or modifications of biased estimators (including Liu estimators) can be used to reduce the estimated variance of the regression coefficients estimates. In this paper, the combination of the biased Liu estimator and stochastic linear restrictions estimator is considered to overcome the effect of collinearity on the estimated coefficients. In addition, the deletion formulas for the detection of influential observations are presented for the proposed estimator. Finally, a simulation study and numerical example have been conducted to show the superiority of the proposed procedures.