On the Ambiguous Consequences of Omitting Variables
This paper studies what happens when we move from a short regression to a long regression (or vice versa), when the long regression is shorter than the data-generation process. In the special case where the long regression equals the data-generation process, the least-squares estimators have smaller bias (in fact zero bias) but larger variances in the long regression than in the short regression. But if the long regression is also misspecified, the bias may not be smaller. We provide bias and mean squared error comparisons and study the dependence of the differences on the misspecification parameter.
|Date of creation:||22 May 2015|
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- Jan R. Magnus & Giuseppe De Luca, 2016. "Weighted-Average Least Squares (Wals): A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 30(1), pages 117-148, 02.
- Holly, Alberto, 1982. "A Remark on Hausman's Specification Test," Econometrica, Econometric Society, vol. 50(3), pages 749-759, May.
- Garber, Steven & Klepper, Steven, 1980. "Extending the Classical Normal Errors-in-Variables Model," Econometrica, Econometric Society, vol. 48(6), pages 1541-1546, September.
- Kevin A. Clarke, 2005. "The Phantom Menace: Omitted Variable Bias in Econometric Research," Conflict Management and Peace Science, Peace Science Society (International), vol. 22(4), pages 341-352, September. Full references (including those not matched with items on IDEAS)
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