Robustness checks and robustness tests in applied economics

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 inte...

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Bibliographic Details
Published in:	Journal of econometrics 2014-01, Vol.178 (1), p.194-206
Main Authors:	Lu, Xun, White, Halbert
Format:	Article
Language:	eng
Subjects:	Causal effect Coefficient of correlation Combined estimator Comparative analysis Conditional exogeneity Econometric models Economic theory Economics Estimating techniques Estimation Model testing Regression analysis Robustness Specification test Statistical methods Stochastic models Validation studies Validity
Online Access:	Get full text
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Summary:	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.
ISSN:	0304-4076 1872-6895