ON SIZE AND POWER OF HETEROSKEDASTICITY AND AUTOCORRELATION ROBUST TESTS
异方差与自相关稳健检验的检验水平和功效AI 解读 ↓
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异方差与自相关稳健检验的检验水平和功效
- 研究问题
- 用于线性回归限制检验的异方差与自相关稳健方法，在有限样本中是否仍可能出现极端检验水平失真或最坏情形功效退化？
- 方法与思路
- 作者建立一大类稳健检验的有限样本理论，分析协方差模型与设计矩阵的作用，并提出加入人工回归变量的调整程序。
- 材料陈述的贡献
- 摘要称，在所述条件下多类修正检验的检验水平为一或干扰参数下功效下确界为零；人工回归变量调整能在许多情形下改善问题。
- 阅读关注点
- 应核对极端结果所依赖的协方差模型和设计条件、最坏情形功效的定义，以及调整在哪些情形下控制水平并使功效远离零。
- 阅读边界
- 本导读仅据所给 Crossref 摘要；具体检验类别、协方差限制及调整步骤摘要未说明，不能据此认定所有稳健检验必然失效。
原文摘要
Testing restrictions on regression coefficients in linear models often requires correcting the conventional F-test for potential heteroskedasticity or autocorrelation amongst the disturbances, leading to so-called heteroskedasticity and autocorrelation robust test procedures. These procedures have been developed with the purpose of attenuating size distortions and power deficiencies present for the uncorrected F-test. We develop a general theory to establish positive as well as negative finite-sample results concerning the size and power properties of a large class of heteroskedasticity and autocorrelation robust tests. Using these results we show that nonparametrically as well as parametrically corrected F-type tests in time series regression models with stationary disturbances have either size equal to one or nuisance-infimal power equal to zero under very weak assumptions on the covariance model and under generic conditions on the design matrix. In addition we suggest an adjustment procedure based on artificial regressors. This adjustment resolves the problem in many cases in that the so-adjusted tests do not suffer from size distortions. At the same time their power function is bounded away from zero. As a second application we discuss the case of heteroskedastic disturbances.