The aim of this paper is to investigate estimation and inference on a low-dimensional causal parameter in the presence of high-dimensional controls in an instrumental variable quantile regression. The estimation and inference are based on the Neyman-type orthogonal moment conditions , that are relatively insensitive to the estimation of the nuisance parameters. The Monte Carlo experiments show that the econometric procedure performs well . We also apply the procedure to reinvestigate two empirical studies: the quantile treatment effect of 401(k) participation on accumulated wealth , and the distributional effect of job-training program participation on trainee earnings .
The aim of this paper is to investigate estimation and inference on a low-dimensional causal parameter in the presence of high-dimensional controls in an instrumental variable quantile regression. The proposed econometric procedure is based on the Neyman-type orthogonal moment conditions tentatively outlined in Chernozhukov, Hansen and Wuthrich (2018) and 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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estimation and inference are
proposed econometric procedure is
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, that are
tentatively outlined in Chernozhukov, Hansen and Wuthrich (2018) and thus
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econometric procedure performs well
estimator copes well with high-dimensional controls
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reinvestigate two empirical studies:
empirically reinvestigate
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, and the distributional effect of job-training program participation on trainee earnings