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Two Sample Unconditional Quantile Effect

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  • Atsushi Inoue
  • Tong Li
  • Qi Xu

Abstract

This paper proposes a new framework to evaluate unconditional quantile effects (UQE) in a data combination model. The UQE measures the effect of a marginal counterfactual change in the unconditional distribution of a covariate on quantiles of the unconditional distribution of a target outcome. Under rank similarity and conditional independence assumptions, we provide a set of identification results for UQEs when the target covariate is continuously distributed and when it is discrete, respectively. Based on these identification results, we propose semiparametric estimators and establish their large sample properties under primitive conditions. Applying our method to a variant of Mincer's earnings function, we study the counterfactual quantile effect of actual work experience on income.

Suggested Citation

  • Atsushi Inoue & Tong Li & Qi Xu, 2021. "Two Sample Unconditional Quantile Effect," Papers 2105.09445, arXiv.org.
    • Handle: RePEc:arx:papers:2105.09445
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    References listed on IDEAS

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    Cited by:

    1. Martinez-Iriarte, Julian & Montes-Rojas, Gabriel & Sun, Yixiao, 2022. "Location-Scale and Compensated Effects in Unconditional Quantile Regressions," University of California at San Diego, Economics Working Paper Series qt89z1w74z, Department of Economics, UC San Diego.
    2. Javier Alejo & Antonio F. Galvao & Julián Martinez-Iriarte & Gabriel Montes-Rojas, 2023. "Unconditional Quantile Partial Effects via Conditional Quantile Regression," Working Papers 217, Red Nacional de Investigadores en Economía (RedNIE).

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