Diagnostic Measures in Ridge Regression Model with AR(1) Errors under the Stochastic Linear Restrictions

Abstract

Outliers and influential observations have important effects on the regression analysis. The goal of this paper is to extend the mean-shift model for detecting outliers in case of ridge regression model in the presence of stochastic linear restrictions when the error terms follow by an autoregressive AR(1) process. Furthermore, extensions of measures for diagnosing influential observations are derived. A numerical example of a real data set is used to illustrate the findings. Finally, a simulation study is conducted to evaluate the performance of the proposed procedure and measures. Results of this study show the efficiency of the proposed mean-shift outlier model for the proposed model. Also, the study resulted in some findings about the behavior of suggested measures for the specified model. In fact, these measures are affected by the degree of collinearity and the size of autocorrelation.