Small-sample one-sided testing in extreme value regression models

TL;DR

This paper develops adjusted signed likelihood ratio tests for extreme value regression models, improving finite-sample accuracy and reducing size distortion through Monte Carlo simulations and real data applications.

Contribution

It introduces adjusted signed likelihood ratio statistics specifically for extreme value regression models, enhancing test accuracy in small samples.

Findings

Adjusted tests reduce size distortion in small samples

Simulations show improved accuracy over standard tests

Real data applications demonstrate practical effectiveness

Abstract

We derive adjusted signed likelihood ratio statistics for a general class of extreme value regression models. The adjustments reduce the error in the standard normal approximation to the distribution of the signed likelihood ratio statistic. We use Monte Carlo simulations to compare the finite-sample performance of the different tests. Our simulations suggest that the signed likelihood ratio test tends to be liberal when the sample size is not large, and that the adjustments are effective in shrinking the size distortion. Two real data applications are presented and discussed.