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In this paper, we consider the beta prime regression model recently proposedby \cite{bour18}, which is tailored to situations where the response iscontinuous and restricted to the positive real line with skewed and long tailsand the regression structure involves regressors and unknown parameters. Weconsider two different strategies of bias correction of the maximum-likelihoodestimators for the parameters that index the model. In particular, we discussbias-corrected estimators for the mean and the dispersion parameters of themodel. Furthermore, as an alternative to the two analytically bias-correctedestimators discussed, we consider a bias correction mechanism based on theparametric bootstrap. The numerical results show that the bias correctionscheme yields nearly unbiased estimates. An example with real data is presentedand discussed.

Improved estimators in beta prime regression models