论文标题
具有高度持久预测指标的半参数测试
Semiparametric Testing with Highly Persistent Predictors
论文作者
论文摘要
我们通过高度持久的预测指标解决了双变量回归问题中半参数效率的问题，其中创新的联合分布被认为是无限维度的滋扰参数。利用极限实验的结构表示并利用其中的不变性关系，我们为感兴趣的回归系数构建了不变的点 - 最佳测试。这种方法自然会导致基于创新的组成部分的可行测试家族，与非高斯创新分布相比，相对于现有测试，可以在高斯性下表现等值。当I.I.D.对创新的假设适合当前数据，我们的测试利用了效率的提高。此外，我们通过模拟表明，我们的测试在某些形式的条件异质性下保持良好状态。
We address the issue of semiparametric efficiency in the bivariate regression problem with a highly persistent predictor, where the joint distribution of the innovations is regarded an infinite-dimensional nuisance parameter. Using a structural representation of the limit experiment and exploiting invariance relationships therein, we construct invariant point-optimal tests for the regression coefficient of interest. This approach naturally leads to a family of feasible tests based on the component-wise ranks of the innovations that can gain considerable power relative to existing tests under non-Gaussian innovation distributions, while behaving equivalently under Gaussianity. When an i.i.d. assumption on the innovations is appropriate for the data at hand, our tests exploit the efficiency gains possible. Moreover, we show by simulation that our test remains well behaved under some forms of conditional heteroskedasticity.
