Probit with Dependent Observations
针对含相依观测值的受限因变量模型，本文提出一种计算简便且相对高效的广义条件矩估计方法，适用于已知或未知扰动协方差矩阵的情形，对计量经济学和实证研究者有参考价值。
Estimation of limited dependent variable models with dependent observations has received relatively little attention due to the computational complexity of the maximum likelihood estimator. We develop a computationally attractive and relatively efficient estimator for this case that utilises the orthogonality conditions. The resulting Generalized Conditional Moment (GCM) estimators can be applied with a known or an unknown disturbance covariance matrix. Although the paper considers only the probit model, the approach is easily generalized to other limited dependent variable models.