Estimation in a Class of Simultaneous Equation Limited Dependent Variable Models
研究一类联立方程受限因变量模型的估计方法，比较了边际和条件最大似然估计的渐近相对效率，并证明了联立方程Tobit似然函数的单峰性，通过蒙特卡洛实验检验了估计量的效率。
Estimation in a class of simultaneous equation limited dependent variable models is considered. The minimum Chi-squared method is used to compare the asymptotic relative efficiency of marginal and new conditional maximum likelihood estimators for this class of models. Efficient minimum Chi-squared estimation procedures are described. A two-step algorithm based on a conditional maximum likelihood estimator provides a natural framework for both computing a linearized and locating the joint maximum likelihood estimator. The unimodality of the simultaneous equation tobit likelihood function is proved and this model is used to illustrate the empirical application of some of the estimators considered in the paper. The relative efficiency of these estimators in the simultaneous equation tobit model is examined in a set of Monte-Carlo experiments.