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Unconditional Quantile Regressions

Sergio Firpo, Nicole Fortin and Thomas Lemieux ()

No 339, NBER Technical Working Papers from National Bureau of Economic Research, Inc

Abstract: 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.

JEL-codes: C14 C21 J31 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm
Date: 2007-07
Note: LS TWP
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Published as Econometrica Volume 77, Issue 3, pages 953–973, May 2009

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