Journal
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
Volume 38, Issue 2, Pages 368-401Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/03610910802506630
Keywords
Autocorrelation; Mean square error; Multicollinearity; Ridge regression; Mixed estimation
Categories
Ask authors/readers for more resources
The presence of autocorrelation in errors and multicollinearity among the regressors has undesirable effects on the least squares regression. There are a wide range of methods, such as the mixed estimator or the ridge estimator, for estimating regression equations, which are aimed to overcome the usefulness of the ordinary least squares estimator or the generalized least squares estimator. The purpose of this article is to examine multicollinearity and autocorrelation problems simultaneously and, to compare the mixed estimator to the ridge regression estimator (RRE) by the dispersion and mse matrix criterions in the linear regression model with correlated or heteroscedastic errors.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available