On Least Squares Estimation when the Dependent Variable is Grouped
研究因变量仅观测到落在连续尺度上的某个区间（实际值未知）时，如何估计线性模型参数。描述了一种获得最大似然估计的最小二乘算法，推导了OLS估计的渐近偏差，提出了一种结合两种方法的“两步估计器”，并在经济实例和模拟实验中表现良好。
This paper examines the problem of estimating the parameters of an underlying linear model using data in which the dependent variable is only observed to fall in a certain interval on a continuous scale, its actual value remaining unobserved. A Least Squares algorithm for attaining the Maximum Likelihood estimator is described, the asymptotic bias of the OLS estimator derived for the normal regressors case and a "moment" estimator presented. A "two-step estimator" based on combining the two approaches is proposed and found to perform well in both an economic illustration and simulation experiments.