Document Type
Article
Publication Date
2005
Published In
American Economist
Abstract
The Generalized Least Squares (GLS) transformation that eliminates serial correlation in the error terms is central to a complete understanding of the relationship between the pooled OLS, random effects, and fixed effects estimators. A significant hurdle to attainment of that understanding is the calculation of the parameter that delivers the desired transformation. This paper derives this critical parameter in the benchmark case typically used to introduce these estimators using nothing more than elementary statistics (mean, variance, and covariance) and the quadratic formula.
Recommended Citation
Philip N. Jefferson.
(2005).
"Deriving The GLS Transformation Parameter In Elementary Panel Data Models".
American Economist.
Volume 49,
Issue 1.
45-48.
DOI: 10.1177/056943450504900103
https://works.swarthmore.edu/fac-economics/11
Comments
This work is freely available courtesy of Omicron Delta Epsilon.