Modeling Income Data via New Parametric Quantile Regressions: Formulation, Computational Statistics, and Application

Income modeling is crucial in determining workers’ earnings and an important research topic labor economics. Traditional regressions based on normal distributions are statistical models widely applied. However, income data have asymmetric behavior best modeled by non-normal distributions. The objective of this work to propose parametric quantile two distributions: Dagum Singh–Maddala. proposed regression reparameterizations the original inserting a parameter. We present reparameterizations, properties distributions, with their inferential aspects. proceed Monte Carlo simulation studies, considering performance evaluation maximum likelihood estimation analysis empirical distribution types residuals. results show that both meet expected outcomes. apply household set provided National Institute Statistics Chile. good model fitting. Thus, we conclude obtained favor Singh–Maddala for positive asymmetrically distributed related incomes. economic implications our investigation discussed final section. Hence, proposal can be valuable addition tool-kit applied statisticians econometricians.