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Estimation and Inference of Distributional Partial Effects: Theory and Application

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  • Shu Shen

Abstract

This article considers nonparametric and semiparametric estimation and inference of the effects of a covariate, either discrete or continuous, on the conditional distribution of a response outcome. It also proposes various uniform tests following estimation. This type of analysis is useful in situations where the econometrician or policy-maker is interested in knowing the effect of a variable or policy on the whole distribution of the response outcome conditional on covariates and is not willing to make parametric functional form assumptions. Monte Carlo experiments show that the proposed estimators and tests are well-behaved in small samples. The empirical section studies the effect of minimum wage hikes on household labor earnings. It is found that the minimum wage has a heterogenous impact on household earnings in the U.S. and that small hikes in the minimum wage are more effective in improving the household earnings distribution.

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  • Shu Shen, 2019. "Estimation and Inference of Distributional Partial Effects: Theory and Application," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(1), pages 54-66, January.
    • Handle: RePEc:taf:jnlbes:v:37:y:2019:i:1:p:54-66
    DOI: 10.1080/07350015.2016.1272458
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    Cited by:

    1. Sungwon Lee, 2021. "Partial Identification and Inference for Conditional Distributions of Treatment Effects," Papers 2108.00723, arXiv.org, revised Nov 2023.
    2. Sungwon Lee, 2024. "Partial identification and inference for conditional distributions of treatment effects," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(1), pages 107-127, January.

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