Gelbach when do covariates matter

National Longitudinal Surveys. Advanced Search. Author: Gelbach, Jonah B. Resulting in 2 citations. Bibliography Citation. Washington, DC www. NLS User Services usersvc chrr.

Gelbach, Jonah B. When Do Covariates Matter? And Which Ones, and How Much? Many authors add variables sequentially to their covariate sets when using linear estimators to investigate the effect of a variable of interest X1, on some outcome y.

One justification for this practice involves robustness: if estimates of the coefficient on X1 are stable across specifications, then researchers conclude that their findings are robust. A second justification involves accounting: by measuring the difference in X1's estimated coefficient as they add sets of covariates to the specification, researchers sometimes claim to have measured the effects of covariate variation on this coefficient. In this paper, I show that sequential covariate addition can be very misleading.

Citation Type. Has PDF. Publication Type. More Filters. When Do Covariates Matter? This is … Expand. A convenient omitted variable bias formula for treatment effect models. Generally, determining the size and magnitude of the omitted variable bias OVB in regression models is challenging when multiple included and omitted variables are present.

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ABSTRACT Researchers frequently test identifying assumptions in regression-based research designs which include instrumental variables or difference-in-differences models by adding additional … Expand. View 3 excerpts, cites background. A common approach to evaluating robustness to omitted variable bias is to observe coefficient movements after inclusion of controls. This is informative only if selection on observables is … Expand.

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View 5 excerpts, cites methods. Unexplained Gaps and Oaxaca-Blinder Decompositions. We analyze four methods to measure unexplained gaps in mean outcomes: three decompositions based on the seminal work of Oaxaca and Blinder and an approach involving a seemingly naive … Expand. View 2 excerpts, references background. Most Difference-in-Difference DD papers rely on many years of data and focus on serially correlated outcomes.

Yet almost all these papers ignore the bias in the estimated standard errors that … Expand. We develop a general approach to robust inference about a scalar parameter of interest when the data is potentially heterogeneous and correlated in a largely unknown way. The key ingredient is the … Expand.