Corrected likelihood ratio tests in beta-prime regression models.
Beta-prime regression models; Likelihood ratio test; Bartlett correction; Skovgaard
adjustment; Bartlett-bootstrap correction.
This paper deals with the issue of testing hypotheses in beta-prime regression models in small
and moderate-sized samples. We focus on the likelihood ratio test, which is unreliable when
the sample size is not large enough to guarantee a good approximation between the exact
distribution of the test statistic and the corresponding chi-squared asymptotic distribution.
Bartlett, Skovgaard and Bartlett-bootstrap corrections typically attenuate the size distortion of
the test. Here, we derive a Bartlett correction and a Skovgaard adjustment for the likelihood
ratio test on beta-prime regression models. We numerically compare the usual, corrected and
bootstrapped tests, through simulations. Our results suggest that the corrected and
bootstrapped tests exhibit type I probability error closer to the chosen nominal level. The
analytically corrected tests perform with the advantage of not requiring computationally
intensive calculations. We present a real data application to illustrate the usefulness of the
modified tests.