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Unconditional Quantile Regressions

Listed author(s):
  • Sergio Firpo
  • Nicole M. Fortin
  • Thomas Lemieux

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.

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File URL: http://www.nber.org/papers/t0339.pdf
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Paper provided by National Bureau of Economic Research, Inc in its series NBER Technical Working Papers with number 0339.

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Date of creation: Jul 2007
Publication status: published as Econometrica Volume 77, Issue 3, pages 953–973, May 2009
Handle: RePEc:nbr:nberte:0339
Note: LS TWP
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