Unconditional quantile regressions / Sergio Firpo, Nicole M. Fortin, Thomas Lemieux.

Author/creator Firpo, Sergio
Other author Fortin, Nicole M.
Other author Lemieux, Thomas.
Other author National Bureau of Economic Research.
Format Electronic
Publication InfoCambridge, MA : National Bureau of Economic Research,
Supplemental ContentFull text available from NBER Working Papers

SeriesNBER working paper series ; working paper . 339
Working paper series (National Bureau of Economic Research : Online) ; working paper no. . 339. UNAUTHORIZED
Summary "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.
General noteTitle from PDF file as viewed on 8/16/2007.
Bibliography noteIncludes bibliographical references.
Access restrictionAvailable only to authorized users.
Other formsAlso available in print.
Technical detailsMode of access: World Wide Web
Genre/formElectronic books.
LCCN 2007616404

Availability

Library Location Call Number Status Item Actions
Electronic Resources Access Content Online ✔ Available