论文标题
线性回归中亚矢量推断的可靠排列测试
A Robust Permutation Test for Subvector Inference in Linear Regressions
论文作者
论文摘要
我们为线性模型中系数子向量推断的新置换测试开发了新的置换测试。当回归器和误差项是独立的时，测试是准确的。然后，我们表明，当独立性条件放松时，在两个主要条件下，该测试是渐近水平，一致的，并具有对局部替代方案的功率。第一个是对回归器与误差项之间通常没有相关性的略有加强。第二个是与样本量相比，由不涉及子向量测试的回归变量的值定义的地层数很少。后者意味着滋扰回归器的向量是离散的。模拟和经验例证表明，如果实际上地层的数量与样本量相比，则该测试在实践中具有良好的力量。
We develop a new permutation test for inference on a subvector of coefficients in linear models. The test is exact when the regressors and the error terms are independent. Then, we show that the test is asymptotically of correct level, consistent and has power against local alternatives when the independence condition is relaxed, under two main conditions. The first is a slight reinforcement of the usual absence of correlation between the regressors and the error term. The second is that the number of strata, defined by values of the regressors not involved in the subvector test, is small compared to the sample size. The latter implies that the vector of nuisance regressors is discrete. Simulations and empirical illustrations suggest that the test has good power in practice if, indeed, the number of strata is small compared to the sample size.
