Ridge estimation in regression problems with autocorrelated errors: A monte carlo study |
| |
| Authors: | Barbara J Gosling Martin L Puterman |
| |
| Institution: | 1. Academic Computing Services , The University of Calgary , Calgary, Alberta, Canada;2. Faculty of Commerce and Business Administration , The University of British Columbia , Vancouver, B.C., Canada |
| |
| Abstract: | This paper presents the results of a Monte Carlo study of OLS and GLS based adaptive ridge estimators for regression problems in which the independent variables are collinear and the errors are autocorrelated. It studies the effects of degree of collinearity, magnitude of error variance, orientation of the parameter vector and serial correlation of the independent variables on the mean squared error performance of these estimators. Results suggest that such estimators produce greatly improved performance in favorable portions of the parameter space. The GLS based methods are best when the independent variables are also serially correlated. |
| |
| Keywords: | ridge regression collinearity serial correlation simulation generalized least squares |
|
|
