Unconditional Quantile Regressions /
Firpo, Sergio.
Unconditional Quantile Regressions / Sergio Firpo, Nicole M. Fortin, Thomas Lemieux. - Cambridge, Mass. National Bureau of Economic Research 2007. - 1 online resource: illustrations (black and white); - NBER technical working paper series no. t0339 . - Technical Working Paper Series (National Bureau of Economic Research) no. t0339. .
July 2007.
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.
System requirements: Adobe [Acrobat] Reader required for PDF files.
Mode of access: World Wide Web.
Unconditional Quantile Regressions / Sergio Firpo, Nicole M. Fortin, Thomas Lemieux. - Cambridge, Mass. National Bureau of Economic Research 2007. - 1 online resource: illustrations (black and white); - NBER technical working paper series no. t0339 . - Technical Working Paper Series (National Bureau of Economic Research) no. t0339. .
July 2007.
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.
System requirements: Adobe [Acrobat] Reader required for PDF files.
Mode of access: World Wide Web.