Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Econometrics |
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| Online Access: | https://www.mdpi.com/2225-1146/9/2/15 |
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doaj-f987fe16bf564c1d9668b32187b3f36c2021-04-02T23:06:24ZengMDPI AGEconometrics2225-11462021-04-019151510.3390/econometrics9020015Debiased/Double Machine Learning for Instrumental Variable Quantile RegressionsJau-er Chen0Chien-Hsun Huang1Jia-Jyun Tien2Institute for International Strategy, Tokyo International University, 1-13-1 Matobakita Kawagoe, Saitama 350-1197, JapanThe Office of the Chief Economist, Microsoft Research, Redmond, Washington, DC 98052, USADepartment of Economics, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, TaiwanIn 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.https://www.mdpi.com/2225-1146/9/2/15quantile treatment effectinstrumental variablequantile regressiondouble machine learninglasso |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Jau-er Chen Chien-Hsun Huang Jia-Jyun Tien |
| spellingShingle |
Jau-er Chen Chien-Hsun Huang Jia-Jyun Tien Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions Econometrics quantile treatment effect instrumental variable quantile regression double machine learning lasso |
| author_facet |
Jau-er Chen Chien-Hsun Huang Jia-Jyun Tien |
| author_sort |
Jau-er Chen |
| title |
Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions |
| title_short |
Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions |
| title_full |
Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions |
| title_fullStr |
Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions |
| title_full_unstemmed |
Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions |
| title_sort |
debiased/double machine learning for instrumental variable quantile regressions |
| publisher |
MDPI AG |
| series |
Econometrics |
| issn |
2225-1146 |
| publishDate |
2021-04-01 |
| description |
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. |
| topic |
quantile treatment effect instrumental variable quantile regression double machine learning lasso |
| url |
https://www.mdpi.com/2225-1146/9/2/15 |
| work_keys_str_mv |
AT jauerchen debiaseddoublemachinelearningforinstrumentalvariablequantileregressions AT chienhsunhuang debiaseddoublemachinelearningforinstrumentalvariablequantileregressions AT jiajyuntien debiaseddoublemachinelearningforinstrumentalvariablequantileregressions |
| _version_ |
1721544572153102336 |