  The inflated beta regression model aims to enable the modeling of responses
in the intervals $(0,1]$, $[0,1)$ or $[0,1]$. In this model, hypothesis testing
is often performed based on the likelihood ratio statistic. The critical values
are obtained from asymptotic approximations, which may lead to distortions of
size in small samples. In this sense, this paper proposes the bootstrap
Bartlett correction to the statistic of likelihood ratio in the inflated beta
regression model. The proposed adjustment only requires a simple Monte Carlo
simulation. Through extensive Monte Carlo simulations the finite sample
performance (size and power) of the proposed corrected test is compared to the
usual likelihood ratio test and the Skovgaard adjustment already proposed in
the literature. The numerical results evidence that inference based on the
proposed correction is much more reliable than that based on the usual
likelihood ratio statistics and the Skovgaard adjustment. At the end of the
work, an application to real data is also presented.
