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Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions
Econometrics Pub Date : 2021-04-02 , DOI: 10.3390/econometrics9020015
Jau-er Chen , Chien-Hsun Huang , Jia-Jyun Tien

In this study, we investigate the 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 et al. 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.

中文翻译:

用于仪器变量分位数回归的去偏/双重机器学习

在这项研究中,我们调查了在工具变量分位数回归中存在高维控件的情况下对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先前研究的Neyman型正交矩条件(Chernozhukov等人2018)的基础上,因此对扰动参数的估计相对不敏感。蒙特卡洛实验表明,估计器可以很好地应对高维控制。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。
更新日期:2021-04-02
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