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When is it really justifiable to ignore explanatory variable endogeneity in a regression model?

Jan Kiviet

No 15-05, UvA-Econometrics Working Papers from Universiteit van Amsterdam, Dept. of Econometrics

Abstract: A conversion of standard ordinary least-squares results into inference which is robust under endogeneity of some regressors has been put forward in Ashley and Parmeter, Economics Letters, 137 (2015) 70-74. However, their conversion is based on an incorrect (though by accident conservative) asymptotic approximation and entails a neglected but avoidable randomness. By a very basic example it is illustrated why a much more sophisticated asymptotic expansion under a stricter set of assumptions is required than used by these authors. Next, particular aspects of their consequently .awed sensitivity analysis for an empirical growth model are replaced by results based on a proper limiting distribution for a feasible inconsistency corrected least-squares estimator. Finally we provide references to literature where relevant asymptotic approximations have been derived which should enable to produce similar endogeneity robust inference for more general models and hypotheses than currently available.

Date: 2015-12-05
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Related works:
Journal Article: When is it really justifiable to ignore explanatory variable endogeneity in a regression model? (2016) Downloads
Working Paper: When is it really justifiable to ignore explanatory variable endogeneity in a regression model? (2016) Downloads
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