Unconditional quantile regressions
By: Sergio Firpo, Nicole M. Fortin, Thomas Lemieux
Contributors: Fortin, Nicole M.; Lemieux, Thomas; National Bureau of Economic Research
Series: NBER working paper series -- working paper . 339; Working paper series (National Bureau of Economic Research : Online) -- working paper no. . 339.
Publisher: National Bureau of Economic Research
Published: 2007
Place of publication: Cambridge, MA
Language: eng
Format: [electronic resource] /
Description: "We propose a new regression method to estimate the impact of explanatory variables on quantiles of the unconditional (marginal) distribution of an outcome variable.  The proposed method consists of running a regression of the (recentered) influence function (RIF) of the unconditional quantile on the explanatory variables.  The influence function is a widely used tool in robust estimation that can easily be computed for each quantile of interest.  We show how standard partial effects, as well as policy effects, can be estimated using our regression approach.  We propose three different regression estimators based on a standard OLS regression (RIF-OLS), a logit regression (RIF-Logit), and a nonparametric logit regression (RIF-OLS).  We also discuss how our approach can be generalized to other distributional statistics besides quantiles"--National Bureau of Economic Research web site.
Notes: Title from PDF file as viewed on 8/16/2007.

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