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Unconditional Quantile Regressions / Sergio Firpo, Nicole M. Fortin, Thomas Lemieux.
- Format:
- Book
- Author/Creator:
- Firpo, Sergio.
- Series:
- Technical Working Paper Series (National Bureau of Economic Research) no. t0339.
- NBER technical working paper series no. t0339
- Language:
- English
- Physical Description:
- 1 online resource: illustrations (black and white);
- Place of Publication:
- Cambridge, Mass. National Bureau of Economic Research 2007.
- 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.
- Notes:
- Print version record
- July 2007.
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