Uniform Inference for Parameters Identified by Conditional Quantile Restrictions
条件分位数限制识别参数的一致推断AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确以条件分位数限制、跨分位数指标聚合及其参数推断为核心，直接对应分位数方向，具体结构模型未说明。
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条件分位数限制识别参数的一致推断
- 研究问题
- 经济模型参数由条件分位数限制识别时，如何跨分位数汇总检验证据，并正确处理预估干扰参数带来的额外不确定性？
- 方法与思路
- 作者以指数加权将条件限制转为连续族无条件矩，构造自适应 ℓ₁ 惩罚上确界统计量，并修正代入方差及使用高斯乘子 bootstrap。
- 材料陈述的贡献
- 摘要称在所述一致性条件及已知参数情形下，自适应选择的最小最大局部功效不低于无惩罚检验，并给出干扰参数预估扩展。
- 阅读关注点
- 核对一致性条件、惩罚选择准则、严格局部功效增益的附加条件，以及代入项、方差修正和 CvM-KS 聚合的定义。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；严格功效增益有附加条件，模拟比较依设计和方向而异，不能据此声称全面更优。
原文摘要
Many structural and dynamic economic models imply that key parameters are identified by conditional quantile restrictions. Building on the exponential-weighting approach of Bierens (1990) and recent advances in penalized maximum statistics for conditional moment restrictions (Chen et al., 2025), we develop a unified inference framework for such parameters. We propose an adaptive $\ell_1$-penalized supremum statistic that transforms the conditional restriction into a continuum of unconditional moment conditions and aggregates evidence across quantile indices. The penalty regularizes the maximization over the weighting direction. Under the stated uniformity conditions, the known-parameter adaptive selector has no lower maximin local power than the unpenalized test and yields a strict maximin local-power gain whenever some positive candidate penalty has a strictly larger population maximin criterion than the zero penalty. We extend the theory to settings with pre-estimated nuisance parameters, characterizing the additional terms induced by the plug-in step in the limiting process. We derive an analytically corrected variance estimator that accounts for plug-in estimation uncertainty and establish the validity of a Gaussian multiplier bootstrap under the null and sequences of local alternatives. Monte Carlo simulations show that the proposed CvM-KS aggregation scheme has rejection rates relatively close to nominal size under pre-estimation in the linear design and approaches the nominal level in nonlinear designs as the sample size grows. The reported power comparisons between the adaptive and unpenalized procedures are design-specific, with higher empirical rejection frequencies for the adaptive procedure along some directions.