Testing for strong serial correlation and dynamic conditional heteroskedasticity in multiple regression
Testing for strong serial correlation and dynamic conditional heteroskedasticity in multiple regression
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DOI：
10.1016/0304-4076(91)90078-r
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发表时间：
1991
影响因子：
6.3
通讯作者：
P. Robinson
中科院分区：
文献类型：
--
作者：
P. Robinson
考虑了回归扰动的序列相关性和/或动态条件异方差性的广泛诊断类别。这些课程包括针对强依赖替代方案具有强大功效的统计数据，以及测试弱依赖替代方案的常用统计数据，等等。在温和的条件下，根据衍生测试的替代方案的依赖结构、扰动矩和回归量，获得限制零分布。各种测试统计量具有相似的整体结构，虽然针对强相关替代方案的测试比针对弱相关替代方案的测试需要更多计算，但如果使用快速傅立叶变换，则差异可能很小。
Broad classes of diagnostics for serial correlation and/or dynamic conditional heteroskedasticity of regression disturbances are considered. The classes include statistics with good power against strongly dependent alternatives, along with the usual ones that test against weak dependence, and many others. Limiting null distributions are obtained, under mild conditions on the dependence structure of the alternative against which the test is derived, on moments of the disturbances, and on the regressors. The various test statistics have a similar overall structure, and while tests against strongly dependent alternatives entail more computation than ones against weakly dependent alternatives, the difference can be slight if the fast Fourier transform is used.