Abstract
We consider the problem of estimating the regression function based on the minimization of the mean squared relative error. We construct an alternative kernel estimate of the regression function. We prove the strong consistency and the asymptotic normality of the constructed estimator under weak dependence conditions. We conduct a simulation study to compare the finite sample performance of our estimator with that of the classical kernel regression.