Birnbaum-Saunders quantile regression and its diagnostics with application to economic data

The Birnbaum-Saunders (BS) distribution is a model that frequently appears in the statistical literature and has proved to be very versatile and efficient across a wide range of applications. However, despite the growing interest in the study of the BS distribution, quantile regression modeling has not been considered for this distribution. To fill this gap, we introduce a class of quantile regression models based on the BS distribution, which allows us to describe positive and asymmetric data when a quantile must be predicted using covariates. We use an approach based on a quantile parameterization to generate the model, permitting us to consider a similar framework to generalized linear models, providing wide flexibility. The methodology proposed includes a thorough study of theoretical properties and practical issues, such as maximum likelihood parameter estimation and diagnostic analytics based on local influence and residuals. The performance of the residuals is evaluated by simulations, whereas an illustrative example of income data is conducted using the methodology to show its potential for applications. The numerical results report an adequate performance of the approach to quantile regression, indicating that the BS distribution is a good modeling choice when dealing with data that have both positive support and asymmetry. The economic implications of our investigation are discussed in the final section. Hence, it can be a valuable addition to the tool kit of applied statisticians and econometricians.

Automated summary

The study introduces a class of quantile regression models based on the Birnbaum-Saunders distribution, providing wide flexibility in modeling positive and asymmetric data. The methodology includes thorough theoretical property study and diagnostic analytics evaluation, with numerical results indicating the adequacy of the approach for quantile regression. The BS distribution is shown to be a good modeling choice for data with positive support and asymmetry.