 Estimation and Inference of Distributional Partial Effects: Theory and ApplicationShu Shen Journal of Business & Economic Statistics, 2019, vol. 37, issue 1, 54-66 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. Date: 2019 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:jnlbes:v:37:y:2019:i:1:p:54-66 Ordering information: This journal article can be ordered from DOI: 10.1080/07350015.2016.1272458 Access Statistics for this article Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals |
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