When Do Covariates Matter? And Which Ones, and How Much?

2016/01/22 by Jonah B. Gelbach · 1 voice · 9 citations
Economics, Econometrics and Finance · Mathematics · #Monetary Policy and Economic Impact #Spatial and Panel Data Analysis #Statistical Methods and Bayesian Inference

paper · doi:10.1086/683668

openalex publication_date 2016/01/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/30

Abstract

Authors often add covariates to a base model sequentially either to test a particular coefficient’s “robustness” or to account for the “effects” on this coefficient of adding covariates. This is problematic, due to sequence sensitivity when added covariates are intercorrelated. Using the omitted variables bias formula, I construct a conditional decomposition that accounts for various covariates’ role in moving base regressors’ coefficients. I also provide a consistent covariance formula. I illustrate this conditional decomposition with NLSY data in an application that exhibits sequence sensitivity. Related extensions include instrumental variables, the fact that my decomposition nests the Oaxaca-Blinder decomposition, and a Hausman test result.

Citations

• Specification Tests in Econometrics

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Discussions

• Can’t Jonah Gelbach’s -b1x2- do this? doi.org/10.1086/683668 [bsky, 3 points, 0 comments]

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