Practical Considerations for Choosing Between Tobit and SCLS or CLAD Estimators for Censored Regression Models with an Application to Charitable Giving*

Authors


  • *

    I am grateful to seminar participants at IUPUI, Adriaan Kalwij, and an anonymous referee for helpful comments. The usual disclaimer applies. I thank David Drukker for kindly sharing the National Longitudinal Survey of Older Women data. I am grateful for support from NICHD Grant Number 1 R01 HD04645633-01. A generous grant from Atlantic Philanthropies to the Center on Philanthropy at Indiana University financed the collection of philanthropy data in the PSID that motivated this research.

Abstract

Practical considerations for choosing between Tobit, symmetrically censored least squares (SCLS) and censored least absolute deviations (CLAD) estimators are offered. Practical considerations deal with when a Hausman test is better than a conditional moment test for judging the severity of a misspecification, the need to bootstrap the sampling distributions of the Hausman tests, what to look for in a graphical examination of the residuals and the limited value of SCLS. The practical considerations are applied to a model of the intergenerational transmission of charitable giving using new data from the Panel Study of Income Dynamics (PSID). The paper shows how to use relative distribution methods to calculate CLAD-based marginal effects on the observable dependent variable.

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