AI 中文总结
针对含高维干扰参数的半参数条件矩模型，开发基于核的规范检验，结合多种方法使推断对干扰估计误差不敏感，建立神谕等价性并刻画局部功效，通过快速乘子自助法避免干扰重估，模拟和应用展示了检验的有限样本性能。
AI 中文摘要
我们为具有高维干扰参数的半参数条件矩模型开发了基于核的规范检验，扩展了现有的条件矩检验，使其能适应现代机器学习方法。所提出的局部稳健核检验结合了奈曼正交矩、交叉拟合和再生核希尔伯特空间方法，使推断对干扰估计误差一阶不敏感。我们在局部备择假设和弱干扰率条件下建立了可行与不可行检验过程之间的神谕等价性，并刻画了局部功效。快速乘子自助法避免了干扰重新估计。应用包括高维线性和逻辑回归中的规范检验、机器学习回归的显著性检验以及恒定条件处理效应的检验。蒙特卡罗模拟和对国家支持工作项目的应用说明了所提检验的有限样本性能。
英文摘要
We develop kernel-based specification tests for semiparametric conditional moment models with high-dimensional nuisance parameters, extending existing conditional moment tests---which typically require asymptotically linear nuisance estimators---to accommodate modern machine-learning methods. The proposed locally robust kernel tests combine Neyman-orthogonal moments, cross-fitting, and reproducing kernel Hilbert space methods, yielding inference that is first-order insensitive to nuisance estimation error. We establish oracle equivalence between the feasible and infeasible test processes under local alternatives and weak nuisance-rate conditions, and characterize the resulting local power. A fast multiplier bootstrap avoids nuisance re-estimation. Applications include specification testing in high-dimensional linear and logistic regression, significance testing with machine-learning regressions, and tests of constant conditional treatment effects. Monte Carlo simulations and an application to the National Supported Work program illustrate the finite-sample performance of the proposed tests.
