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Author Barros, M.; Galea, M.; Leiva, V.; Santos-Neto, M.
Title Generalized Tobit models: diagnostics and application in econometrics Type
Year 2018 Publication Journal Of Applied Statistics Abbreviated Journal J. Appl. Stat.
Volume 45 Issue 1 Pages 145-167
Keywords Cook distance; elliptically contoured distributions; labor supply data; local influence method; maximum likelihood method; R software; residuals; Student-t distribution
Abstract The standard Tobit model is constructed under the assumption of a normal distribution and has been widely applied in econometrics. Atypical/extreme data have a harmful effect on the maximum likelihood estimates of the standard Tobit model parameters. Then, we need to count with diagnostic tools to evaluate the effect of extreme data. If they are detected, we must have available a Tobit model that is robust to this type of data. The family of elliptically contoured distributions has the Laplace, logistic, normal and Student-t cases as some of its members. This family has been largely used for providing generalizations of models based on the normal distribution, with excellent practical results. In particular, because the Student-t distribution has an additional parameter, we can adjust the kurtosis of the data, providing robust estimates against extreme data. We propose a methodology based on a generalization of the standard Tobit model with errors following elliptical distributions. Diagnostics in the Tobit model with elliptical errors are developed. We derive residuals and global/local influence methods considering several perturbation schemes. This is important because different diagnostic methods can detect different atypical data. We implement the proposed methodology in an R package. We illustrate the methodology with real-world econometrical data by using the R package, which shows its potential applications. The Tobit model based on the Student-t distribution with a small quantity of degrees of freedom displays an excellent performance reducing the influence of extreme cases in the maximum likelihood estimates in the application presented. It provides new empirical evidence on the capabilities of the Student-t distribution for accommodation of atypical data.
Address [Barros, Michelli; Santos-Neto, Manoel] Univ Fed Campina Grande, Dept Stat, Campina Grande, Brazil, Email: victorleivasanchez@gmail.com
Corporate Author Thesis
Publisher Taylor & Francis Ltd Place of Publication Editor
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue (up) Edition
ISSN 0266-4763 ISBN Medium
Area Expedition Conference
Notes WOS:000415929600011 Approved
Call Number UAI @ eduardo.moreno @ Serial 781
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