Unconditional Quantile Regressions

Arbeitspapier

We propose a new regression method to estimate the impact of explanatory variables on quantiles of the unconditional 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 (RIFOLS), a Logit regression (RIF-Logit), and a nonparametric Logit regression (RIFNP). We also discuss how our approach can be generalized to other distributional statistics besides quantiles.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Texto para discussão ; No. 533

Classification and Topics

• Classification: Wirtschaft
• Subject: Influence Functions ; Unconditional Quantile ; Quantile Regressions. ; Regressionsanalyse ; Statistische Verteilung ; Schätztheorie ; Theorie

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Firpo, Sergio ; Fortin, Nicole M. ; Lemieux, Thomas
• Event: Veröffentlichung
• (who): Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Departamento de Economia
• (where): Rio de Janeiro
• (when): 2006

Further information

• Handle: http://hdl.handle.net/10419/176016
• Last update: 10.03.2025, 11:41 AM CET