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Unconditional Quantile Regression with High Dimensional Data

Yuya Sasaki, Takuya Ura, Yichong Zhang

arXiv 27 Jul 2020 · Econometrics · publishedQuantitative Economics (2022) · 12 citations (OpenAlex)

arXiv:2007.13659 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper considers estimation and inference for heterogeneous counterfactual effects with high-dimensional data. We propose a novel robust score for debiased estimation of the unconditional quantile regression (Firpo, Fortin, and Lemieux, 2009) as a measure of heterogeneous counterfactual marginal effects. We propose a multiplier bootstrap inference and develop asymptotic theories to guarantee the size control in large sample. Simulation studies support our theories. Applying the proposed method to Job Corps survey data, we find that a policy which counterfactually extends the duration of exposures to the Job Corps training program will be effective especially for the targeted subpopulations of lower potential wage earners.

Citation extraction

17
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Firpo, S., N. M. Fortin, and T. Lemieux (2009) Unconditional quantile regressions1.000104100%
2Belloni, A., V. Chernozhukov, I. Fernández-Val, and C. Hansen (2017) Program Evaluation with High-dimensional Data0.81142100%
3Chernozhukov, V., W. K. Newey, and R. Singh (2021) b): Automatic debiased machine learning of causal and structural effects0.81142100%
4Newey, W. K (1994) The asymptotic variance of semiparametric estimators0.64422100%
5Fortin, N., T. Lemieux, and S. Firpo (2011) Decomposition methods in economics, in0.58531100%
6Belloni, A., V. Chernozhukov, and K. Kato (2014) Uniform post-selection inference for least absolute deviation regression and other Z-estimation problems0.51121100%
7Bickel, P. J., Y. Ritov, and A. B. Tsybakov (2009) Simultaneous Analysis of Lasso and Dantzig Selector0.40511100%
8Chernozhukov, V., D. Chetverikov, and K. Kato (2014) b): Gaussian Approximation of Suprema of Empirical Processes0.40511100%
9Chernozhukov, V., D. Chetverikov, and K. Kato (2014) a): Anti-concentration and Honest, Adaptive Confidence Bands0.40511100%
10Schochet, P. Z., J. Burghardt, and S. McConnell (2008) Does job corps work? Impact findings from the national job corps study0.40511100%

Showing the top 10 of 17 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Debiased Machine Learning of Set-Identified Linear Models0.51121
2Two Sample Unconditional Quantile Effect0.51121
3Unconditional Effects of General Policy Interventions0.51121
4Structural Representations and Identification of Marginal Policy Effects0.51121
5Regularized Orthogonal Machine Learning for Nonlinear Semiparametric Models0.40511
6Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments0.40511
7Generalized Lee Bounds0.40511
8Identification and Estimation of Unconditional Policy Effects of an Endogenous Binary Treatment: An Unconditional MTE Approach0.40511
9Unconditional Quantile Partial Effects via Conditional Quantile Regression0.40511
10Sharp Structure-Agnostic Lower Bounds for General Linear Functional Estimation0.40511