Locally optimal one-sided tests for multiparameter hypotheses
Locally optimal one-sided tests for multiparameter hypotheses
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DOI：
10.1080/07474939708800379
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发表时间：
1997
影响因子：
1.2
通讯作者：
M. King;P. Wu
中科院分区：
文献类型：
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作者：
M. King;P. Wu
最近，人们越来越意识到许多计量经济学测试问题的片面性。本文构造了单一零假设对多参数单边选择的局部最大均方(LMMP)检验。得到的测试统计量是在零假设下评估的分数的总和。这使得使用和不使用滋扰参数都很容易应用。在线性回归模型的情况下，可以使用不变性自变量来处理讨厌的参数，从而允许构造精确的检验。在线性回归模型的背景下考虑的应用包括对非零回归系数、自回归扰动、异方差扰动、随机回归系数和方差分量的联合单边检验。
Recently, there has been an increased awareness of the one-sided nature of many econometric testing problems. This paper constructs a locally most mean powerful (LMMP) test of a silnple null hypothesis against a lnultiparameter one-sided alternative. The resultant test statistic is the sum of the scores evaluated at the null hypothesis. This makes it easy to apply both with and without nuisance parameters. In the case of the linear regression model, invariance arguments can be used to deal with nuisance parameters allowing the construction of exact tests. Applications considered in the context of the linear regression model include joint one-sided testing for non-zero regression coefficients, autoregressive disturbances, heteroscedastic disturbances, random regression coefficients and variance components.