.png){kind=icon}
.png){kind=icon}
Return
BALANCED VARIABLE ADDITION IN LINEAR MODELS
.png){kind=icon}
OA
AI
DOI：10.1111/joes.12245.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Bias amplification
Inconsistency
Least-squares estimators
Mean squared error
Omitted variables
Proxy variables
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
5
Papers:
1.2K
Citations:
5.9K
View more organizations