ESTIMATION WITH CENSORED REGRESSORS: BASIC ISSUES*
研究线性模型中一个回归变量被删失时的估计问题，讨论删除删失观测的效率损失，指出引入虚拟变量不能纠正偏差，并推导混合边界独立删失的似然函数，应用于财富对消费影响的估计。
We study issues that arise for estimation of a linear model when a regressor is censored. We discuss the efficiency losses from dropping censored observations, and illustrate the losses for bound censoring. We show that the common practice of introducing a dummy variable to “correct for” censoring does not correct bias or improve estimation. We show how censored observations generally have zero semiparametric information, and we discuss implications for estimation. We derive the likelihood function for a parametric model of mixed bound‐independent censoring, and apply that model to the estimation of wealth effects on consumption.