r − k Class estimator in the linear regression model with correlated errors

• Published in: Statistical papers (Berlin, Germany) Vol. 55; no. 2; pp. 393 - 407
• Main Authors: Üstündag Siray, Gülesen, Kaçiranlar, Selahattin, Sakalliolu, Sadullah
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-05-2014; Springer Nature B.V
• Subjects: Autocorrelation; Bias; Correlation; Economic Theory/Quantitative Economics/Mathematical Methods; Economics; Eigenvalues; Errors; Estimators; Finance; Insurance; Least squares method; Linear equations; Management; Mathematics and Statistics; Mean square errors; Operations Research/Decision Theory; Probability Theory and Stochastic Processes; Regression; Regression analysis; Regular Article; Scalars; Statistical analysis; Statistics; Statistics for Business; Studies; −; Autocorrelation; Mean square error matrix; Multicollinearity; Class estimator; Ridge regression estimator
• ISSN: 0932-5026; 1613-9798
• DOI: 10.1007/s00362-012-0484-8

Autocorrelation in errors and multicollinearity among the regressors are serious problems in regression analysis. The aim of this paper is to examine multicollinearity and autocorrelation problems concurrently and to compare the
r
−
k
class estimator to the generalized least squares estimator, the principal components regression estimator and the ridge regression estimator by the scalar and matrix mean square error criteria in the linear regression model with correlated errors.