rifexpectile: Density-Free RIF Decompositions for Unconditional Expectiles
Implements a density-free recentered influence function (RIF)
regression framework for unconditional expectiles, and embeds it in a
two-sample Oaxaca-Blinder decomposition indexed continuously by the
expectile level. Unlike quantile-based RIF decompositions, which require
estimating an inverse density term at each quantile, the expectile RIF
depends only on primitive moments of the outcome distribution and
requires no density estimation, no bandwidth selection, and no kernel
smoothing. The package provides expectile estimation by iteratively
reweighted least squares, closed-form RIF construction, two-sample
composition/structure decomposition across a grid of expectile levels,
bootstrap-based inference, and plotting methods. The underlying
methodology is described in Ndoye (2025), "Semi-Nonparametric
Expectile RIF Regression for Distributional Decomposition," presented at
the 2025 World Congress of the Econometric Society, Seoul, Korea,
<https://www.econometricsociety.org/regional-activities/conference-papers/view/282/943>.
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