Imagine White-style robust OLS inference, but with
robustness to endogeneity as opposed to heteroskedasticity/autocorrelation (or
maybe even robustness to all three). It
sounds too good to be true. Actually, it
sounds impossible, and even if somehow possible, it would of course require not
only post-OLS tweaking of standard errors but also post-OLS tweaking of
coefficient estimates. But there are
actually some emerging results – embryonic and requiring strict conditions --
but results nonetheless. Quite
intriguing. Check out Jan Kiviet’s latest paper, "When is it Really Justifiable to Ignore Explanatory Variable Endogeneity in a Regression Model?"
From Twitter: There are interesting antecedents. See Rossi et al., Klein and Vella, several papers by Lewbel et al., inter alia.
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