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Deriving the GLS Transformation Parameter in Elementary Panel Data Models

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  • Philip N. Jefferson

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.

Suggested Citation

  • Philip N. Jefferson, 2005. "Deriving the GLS Transformation Parameter in Elementary Panel Data Models," The American Economist, Sage Publications, vol. 49(1), pages 45-48, March.
    • Handle: RePEc:sae:amerec:v:49:y:2005:i:1:p:45-48
    DOI: 10.1177/056943450504900103
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    1. Ruud, Paul A., 2000. "An Introduction to Classical Econometric Theory," OUP Catalogue, Oxford University Press, number 9780195111644.
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