Optimal sup-norm rates and uniform inference on nonlinear functionals of nonparametric IV regression
非参数工具变量回归的最优上确界范数收敛率与非线性泛函一致推断AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要以工具变量估计结构需求函数，并对消费者剩余和无谓损失等结构福利泛函进行推断。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
非参数工具变量回归的最优上确界范数收敛率与非线性泛函一致推断
- 研究问题
- 如何为非参数工具变量回归中的结构函数及导数建立最优上确界范数收敛率，并对非线性泛函构造一致置信带？
- 方法与思路
- 作者研究筛法非参数工具变量估计，推导极小极大下界，再建立高斯过程强逼近与得分自助法置信带。
- 材料陈述的贡献
- 摘要称样条或小波筛可达到最优收敛率，并在轻度及严重病态问题下，为精确消费者剩余和无谓损失建立渐近推断。
- 阅读关注点
- 应核对病态程度、函数平滑性与筛维数条件，以及置信带对非线性福利泛函的适用范围和汽油需求应用。
- 阅读边界
- 仅据所给出版社摘要，无法判断原始条件的强度、有限样本覆盖率或实际带宽选择；所述优势均有其理论适用条件。
原文摘要
This paper makes several important contributions to the literature about nonparametric instrumental variables (NPIV) estimation and inference on a structural function h0 and functionals of h0. First, we derive sup‐norm convergence rates for computationally simple sieve NPIV (series two‐stage least squares) estimators of h0 and its derivatives. Second, we derive a lower bound that describes the best possible (minimax) sup‐norm rates of estimating h0 and its derivatives, and show that the sieve NPIV estimator can attain the minimax rates when h0 is approximated via a spline or wavelet sieve. Our optimal sup‐norm rates surprisingly coincide with the optimal root‐mean‐squared rates for severely ill‐posed problems, and are only a logarithmic factor slower than the optimal root‐mean‐squared rates for mildly ill‐posed problems. Third, we use our sup‐norm rates to establish the uniform Gaussian process strong approximations and the score bootstrap uniform confidence bands (UCBs) for collections of nonlinear functionals of h0 under primitive conditions, allowing for mildly and severely ill‐posed problems. Fourth, as applications, we obtain the first asymptotic pointwise and uniform inference results for plug‐in sieve t‐statistics of exact consumer surplus (CS) and deadweight loss (DL) welfare functionals under low‐level conditions when demand is estimated via sieve NPIV. Our real data application of UCBs for exact CS and DL functionals of gasoline demand reveals interesting patterns and is applicable to other goods markets.