Practical Considerations for Choosing Between Tobit and SCLS or CLAD Estimators for Censored Regression Models with an Application to Charitable Giving*
提供选择Tobit、对称删失最小二乘（SCLS）和删失最小绝对偏差（CLAD）估计量的实用建议，包括Hausman检验与条件矩检验的比较、自助法应用、残差图形分析，并基于PSID数据研究慈善捐赠的代际传递。
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.