Abstract
This note compares ordinary least squares (OLS) and Gauss-Markov (GM) estimates of regression parameters in linear models when errors are homoscedastic but otherwise arbitrary. It is shown that the efficiency of OLS relative to GM depends crucially on the underlying regressor matrix. This extends and qualifies previous results (Kramer 1980), where errors were confined to be first-order autoregressive. In particular, whenever there is a constant in the regression, it is shown that OLS has limiting efficiency of 1 as correlation increases also in the general case.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
