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摘要翻译：
回归分位数的渐近方差取决于给定回归子的响应变量的条件密度。本文给出了回归分位数渐近方差的一种新估计，它使得任何结果的Wald型检验或置信域在大样本中与它的不可行对应体一样，在真实条件响应密度被嵌入其中。我们给出了实现新方差估计器的明确指导，以自适应地控制任何结果Wald型检验的大小。Monte Carlo证据表明，我们的方法有可能在不同分位数水平、数据生成过程和样本量的情况下，在具有良好大小性能的协变量中提供分位数处理效应异质性的强大测试。我们还包括一个实证例子。补充材料可在网上查阅。
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英文标题：
《Quantile-Regression Inference With Adaptive Control of Size》
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作者：
Juan Carlos Escanciano and Chuan Goh
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最新提交年份：
2019
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
Regression quantiles have asymptotic variances that depend on the conditional densities of the response variable given regressors. This paper develops a new estimate of the asymptotic variance of regression quantiles that leads any resulting Wald-type test or confidence region to behave as well in large samples as its infeasible counterpart in which the true conditional response densities are embedded. We give explicit guidance on implementing the new variance estimator to control adaptively the size of any resulting Wald-type test. Monte Carlo evidence indicates the potential of our approach to deliver powerful tests of heterogeneity of quantile treatment effects in covariates with good size performance over different quantile levels, data-generating processes and sample sizes. We also include an empirical example. Supplementary material is available online.
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PDF链接：
https://arxiv.org/pdf/1807.06977
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