Asymptotic properties of a quast-maximum likelihood estimator in truncated regression model with serial correlation
证明了在带序列相关的截断回归模型中，基于独立误差假设的拟极大似然估计量具有强一致性和渐近正态性，并给出了其极限协方差矩阵的一致估计量。
Abstract Robinson (1982a) presented a general approach to serial correlation in limited dependent variable models and proved the strong consistency and asymptotic normality of the quasi-maximum likelihood estimator (QMLE) for the Tobit model with serial correlation, obtained under the assumption of independent errors. This paper proves the strong consistency and asymptotic normality of the QMLE based on independent errors for the truncated regression model with serial correlation and gives consistent estimators for the limiting covariance matrix of the QMLE. Keywords: tobit modeltruncated regression modelserial correlationquasi-maximum likelihood estimator