An estimator for predictive regression: reliable inference for financial economics


Estimating linear regression using least squares and reporting robust
standard errors is very common in financial economics, and indeed, much of
the social sciences and elsewhere.   For thick tailed predictors under
heteroskedasticity this recipe for inference performs poorly, sometimes
dramatically so. Here, we develop an alternative approach which delivers an
unbiased, consistent and asymptotically normal estimator so long as the
means of the outcome and predictors are finite.  The new method has
standard errors under heteroskedasticity which are easy to reliably estimate
and tests which are close to their nominal size. The procedure works well
in simulations and in an empirical exercise.  An extension is given to
quantile regression.

Last updated on 08/18/2020