Box-Cox power transformation unconditional quantile regressions with an application on wage inequality.

Content Provider	Europe PMC
Author	Ghosh, Pallab Kumar
Copyright Year	2020
Abstract	This study proposes a semi-parametric estimation method, Box–Cox power transformation unconditional quantile regression, to estimate the impact of changes in the distribution of the explanatory variables on the unconditional quantile of the outcome variable. The proposed method consists of running a nonlinear regression of the recentered influence function (RIF) of the outcome variable on the explanatory variables. We also show the asymptotic properties of the proposed estimator and apply the estimation method to address an existing puzzle in labor economics–why the 50th/10th percentile wage gap has been falling in the USA since the late 1980s. Our results show that declining unionization can explain approximately 10% of the decline in the 50/10 wage gap in 1990–2000 and 23% in 2000–2010.
Related Links	https://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC9041873&blobtype=pdf
Page Count	16
ISSN	02664763
Volume Number	48
DOI	10.1080/02664763.2020.1795817
PubMed Central reference number	PMC9041873
Issue Number	16
PubMed reference number	35707252
Journal	Journal of Applied Statistics [J Appl Stat]
e-ISSN	13600532
Language	English
Publisher	Taylor & Francis
Publisher Date	2020-07-21
Access Restriction	Open
Rights License	© 2020 Informa UK Limited, trading as Taylor & Francis Group
Subject Keyword	Unconditional quantile regression extended Box–Cox power transformation unconditional quantile regression Yeo–Johnson RIF regression Unionization 50/10 and 90/50 percentile wage gaps C14 C21 J31
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty