Robustness checks and robustness tests in applied economics

Xun Lu and Halbert White

Journal of Econometrics, 2014, vol. 178, issue P1, 194-206

Abstract: A common exercise in empirical studies is a “robustness check”, where the researcher examines how certain “core” regression coefficient estimates behave when the regression specification is modified by adding or removing regressors. If the coefficients are plausible and robust, this is commonly interpreted as evidence of structural validity. Here, we study when and how one can infer structural validity from coefficient robustness and plausibility. As we show, there are numerous pitfalls, as commonly implemented robustness checks give neither necessary nor sufficient evidence for structural validity. Indeed, if not conducted properly, robustness checks can be completely uninformative or entirely misleading. We discuss how critical and non-critical core variables can be properly specified and how non-core variables for the comparison regression can be chosen to ensure that robustness checks are indeed structurally informative. We provide a straightforward new Hausman (1978) type test of robustness for the critical core coefficients, additional diagnostics that can help explain why robustness test rejection occurs, and a new estimator, the Feasible Optimally combined GLS (FOGLeSs) estimator, that makes relatively efficient use of the robustness check regressions. A new procedure for Matlab, testrob, embodies these methods.

Keywords: Robustness; Causal effect; Conditional exogeneity; Specification test; Combined estimator (search for similar items in EconPapers)
JEL-codes: C18 C51 (search for similar items in EconPapers)
Date: 2014
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (71)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:178:y:2014:i:p1:p:194-206

DOI: 10.1016/j.jeconom.2013.08.016

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