摘要翻译:
在本研究中,我们研究了在一个工具变量分位数回归中,在高维控制存在的情况下,对一个低维因果参数的估计和推断。我们提出的计量经济学程序建立在Chernozhukov、Hansen和Wuthrich(2018)的Neyman型正交矩条件的基础上,因此对公害参数的估计相对不敏感。Monte Carlo实验表明,该估计器能很好地处理高维控制。我们还应用该程序对401(k)参与对累积财富的分位数处理效果进行了实证研究。
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英文标题:
《Debiased/Double Machine Learning for Instrumental Variable Quantile
Regressions》
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作者:
Jau-er Chen, Chien-Hsun Huang, Jia-Jyun Tien
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最新提交年份:
2021
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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:
In this study, we investigate estimation and inference on a low-dimensional causal parameter in the presence of high-dimensional controls in an instrumental variable quantile regression. Our proposed econometric procedure builds on the Neyman-type orthogonal moment conditions of a previous study Chernozhukov, Hansen and Wuthrich (2018) and is thus relatively insensitive to the estimation of the nuisance parameters. The Monte Carlo experiments show that the estimator copes well with high-dimensional controls. We also apply the procedure to empirically reinvestigate the quantile treatment effect of 401(k) participation on accumulated wealth.
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PDF链接:
https://arxiv.org/pdf/1909.12592