Quantitative Economics

Journal Of The Econometric Society

Edited by: Stéphane Bonhomme • Print ISSN: 1759-7323 • Online ISSN: 1759-7331

Quantitative Economics: Jul, 2022, Volume 13, Issue 3

Unconditional quantile regression with high‐dimensional data

Yuya Sasaki, Takuya Ura, Yichong Zhang

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.

Counterfactual analysis debiased machine learning doubly/locally robust score C14 C21

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Supplement to "Unconditional quantile regression with high‐dimensional data"

Supplement to "Unconditional quantile regression with high‐dimensional data"

Supplement to "Unconditional quantile regression with high‐dimensional data"

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