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BALANCED VARIABLE ADDITION IN LINEAR MODELS
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DOI：10.1111/joes.12245.png)
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摘要
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This paper studies what happens when we move from a short regression to a long regression in a setting where both regressions are subject to misspecification. In this setup, the least-squares estimator in the long regression may have larger inconsistency than the least-squares estimator in the short regression. We provide a simple interpretation for the comparison of the inconsistencies and study under which conditions the additional regressors in the long regression represent a balanced addition to the short regression.
Keyword:
Bias amplification
Inconsistency
Least-squares estimators
Mean squared error
Omitted variables
Proxy variables
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