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A practical generalized propensity-score estimator for quantile continuous treatment effects

Author

Listed:
  • Javier Alejo

    (IECON-UdelaR)

  • Antonio F. Galvao

    (University of Arizona)

  • Gabriel Montes-Rojas

    (CONICET-IIEP-UBA)

Abstract

In this article, we present a new command, qcte, that implements several methods for estimation and inference for quantile treatment-effects models with a continuous treatment. We propose a semiparametric two-step estimator, where the first step is based on a flexible Box–Cox model, as the default model of the command. We develop practical statistical inference procedures using boot- strap. We implement some simulations to show that the proposed methods perform well. Finally, we apply qcte to a survey of Massachusetts lottery winners to esti- mate the unconditional quantile effects of the prize amount, as a proxy of nonlabor income changes, on subsequent labor earnings from U.S. Social Security records. The empirical results reveal strong heterogeneity across unconditional quantiles. Copyright 2020 by StataCorp LP.

Suggested Citation

  • Javier Alejo & Antonio F. Galvao & Gabriel Montes-Rojas, 2020. "A practical generalized propensity-score estimator for quantile continuous treatment effects," Stata Journal, StataCorp LP, vol. 20(2), pages 276-296, June.
    • Handle: RePEc:tsj:stataj:v:20:y:2019:i:2:p:276-296
    DOI: 10.1177/1536867X20930997
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