A General Approach to Serial Correlation
提出一个通用框架，涵盖非线性联立方程、Probit、Tobit等模型，将序列相关的得分检验推广为Durbin-Watson统计量的广义形式，并证明最大似然估计对序列相关具有稳健性。
In this paper the testing and estimation problems are discussed in the case of serial correlation. Various models are particular cases of the general framework considered: the nonlinear simultaneous equations models, the probit models, the tobit models, the disequilibrium models, the frontier models, etc. In this context, it is shown that the score test can be written explicitly and that the statistic obtained is a generalization of that of Durbin and Watson; moreover, the maximum likelihood estimation procedure is shown to be robust with respect to serial correlation.