  In this paper, we study the ordinary backfitting and smooth backfitting as
methods of fitting additive quantile models. We show that these backfitting
quantile estimators are asymptotically equivalent to the corresponding
backfitting estimators of the additive components in a specially-designed
additive mean regression model. This implies that the theoretical properties of
the backfitting quantile estimators are not unlike those of backfitting mean
regression estimators. We also assess the finite sample properties of the two
backfitting quantile estimators.
