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. %G eng