Practically significant differences between conditional distribution functions

TL;DR

This paper develops a practical hypothesis test for comparing two conditional distribution functions, focusing on detecting meaningful deviations rather than exact equality, using a self-normalized approach that avoids complex variance estimation.

Contribution

It introduces a novel pivotal test for the $L^2$ distance between conditional distributions, applicable under composite null hypotheses without requiring variance estimation or bootstrap methods.

Findings

The test is asymptotically valid and consistent under local alternatives.

Simulation studies demonstrate the method's effectiveness.

Application to real data shows practical usefulness.

Abstract

In the framework of semiparametric distribution regression, we consider the problem of comparing the conditional distribution functions corresponding to two samples. In contrast to testing for exact equality, we are interested in the (null) hypothesis that the $L^2$ distance between the conditional distribution functions does not exceed a certain threshold in absolute value. The consideration of these hypotheses is motivated by the observation that in applications, it is rare, and perhaps impossible, that a null hypothesis of exact equality is satisfied and that the real question of interest is to detect a practically significant deviation between the two conditional distribution functions. The consideration of a composite null hypothesis makes the testing problem challenging, and in this paper we develop a pivotal test for such hypotheses. Our approach is based on self-normalization and therefore requires neither the estimation of (complicated) variances nor bootstrap approximations. We derive the asymptotic limit distribution of the (appropriately normalized) test statistic and show consistency under local alternatives. A simulation study and an application to German SOEP data reveal the usefulness of the method.