MTests with a New Normalization Matrix
提出一类新的M检验，用递归子样本估计的矩函数构造归一化矩阵替代渐近协方差矩阵，自动处理参数估计影响和一般形式的条件异方差与序列相关，在三个模拟例子中表现出良好的尺寸性质和相当的检验功效。
This paper proposes a new family of M tests building on the work of Kuan and Lee (2006 Kuan , C.-M. , Lee , W.-M. ( 2006 ). Robust M tests without consistent estimation of the asymptotic covariance matrix . Journal of the American Statistical Association 101 : 1264 – 1275 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) and Kiefer et al. (2000 Kiefer , N. M. , Vogelsang , T. J. , Bunzel , H. ( 2000 ). Simple robust testing of regression hypothesis . Econometrica 68 : 695 – 714 .[Crossref], [Web of Science ®] , [Google Scholar]). The idea is to replace the asymptotic covariance matrix in conventional M tests with an alternative normalization matrix, constructed using moment functions estimated from (K + 1) recursive subsamples. The new tests are simple to implement. They automatically account for the effect of parameter estimation and allow for conditional heteroskedasticity and serial correlation of general forms. They converge to central F distributions under the fixed-K asymptotics and to chi-square distributions if K is allowed to approach infinity. We illustrate their applications using three simulation examples: (1) specification testing for conditional heteroskedastic models, (2) non-nested testing with serially correlated errors, and (3) testing for serial correlation with unknown heteroskedasticity. The results show that the new tests exhibit good size properties with power often comparable to the conventional M tests while being substantially higher than that of Kuan and Lee (2006 Kuan , C.-M. , Lee , W.-M. ( 2006 ). Robust M tests without consistent estimation of the asymptotic covariance matrix . Journal of the American Statistical Association 101 : 1264 – 1275 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]).