Testing exogeneity in nonparametric instrumental variables models identified by conditional quantile restrictions

研究问题: 在结构函数通过条件分位数限制识别的非参数IV模型中，如何检验解释变量外生性，同时避免参数形式误设和结构函数估计？
方法与思路: 作者构造不要求结构函数属于已知有限维参数族、也不必估计该函数的外生性检验，并通过模拟和应用展示表现。
材料陈述的贡献: 摘要称，结构函数与条件分位数函数在正概率集合上不同时检验一致，并对 $O({n^{ - 1/2}})$ 偏离具有一致非平凡功效。摘要称，结构函数与条件分位数函数在正概率集合上不同时检验一致，并对$O({n^{ - 1/2}})$偏离具有一致非平凡功效。
阅读关注点: 应核对外生性原假设的精确定义、条件分位数识别要求，以及局部功效结论所覆盖的函数类别与一致性范围。
阅读边界: 所给Crossref摘要未说明统计量形式、临界值构造及工具变量条件；非平凡局部功效不能直接理解为任意备择下的高检验力。

GPT-6.1 Sol · 高 · 2026-10-15

原文摘要

Summary This paper presents a test for exogeneity of explanatory variables in a nonparametric instrumental variables (IV) model whose structural function is identified through a conditional quantile restriction. Quantile regression models are increasingly important in applied econometrics. As with mean-regression models, an erroneous assumption that the explanatory variables in a quantile regression model are exogenous can lead to highly misleading results. In addition, a test of exogeneity based on an incorrectly specified parametric model can produce misleading results. This paper presents a test of exogeneity that does not assume that the structural function belongs to a known finite-dimensional parametric family and does not require estimation of this function. The latter property is important because nonparametric estimates of the structural function are unavoidably imprecise. The test presented here is consistent whenever the structural function differs from the conditional quantile function on a set of nonzero probability. The test has nontrivial power uniformly over a large class of structural functions that differ from the conditional quantile function by $O({n^{ - 1/2}})$ . The results of Monte Carlo experiments and an empirical application illustrate the performance of the test.Summary This paper presents a test for exogeneity of explanatory variables in a nonparametric instrumental variables (IV) model whose structural function is identified through a conditional quantile restriction. Quantile regression models are increasingly important in applied econometrics. As with mean-regression models, an erroneous assumption that the explanatory variables in a quantile regression model are exogenous can lead to highly misleading results. In addition, a test of exogeneity based on an incorrectly specified parametric model can produce misleading results. This paper presents a test of exogeneity that does not assume that the structural function belongs to a known finite-dimensional parametric family and does not require estimation of this function. The latter property is important because nonparametric estimates of the structural function are unavoidably imprecise. The test presented here is consistent whenever the structural function differs from the conditional quantile function on a set of nonzero probability. The test has nontrivial power uniformly over a large class of structural functions that differ from the conditional quantile function by $O({n^{ - 1/2}})$. The results of Monte Carlo experiments and an empirical application illustrate the performance of the test.

摘要来源：Crossref

笔记与分组

分类依据与纠错

Testing exogeneity in nonparametric instrumental variables models identified by conditional quantile restrictions

代码、数据与补充材料

条件分位数限制识别的非参数工具变量模型中的外生性检验

原文摘要

同方向的基础文献

同方向的基础文献