Unconditional Quantile Regressions
AbstractWe propose a new regression method to evaluate the impact of changes in the distribution of the 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, a widely used tool in robust estimation, is easily computed for quantiles, as well as for other distributional statistics. Our approach, thus, can be readily generalized to other distributional statistics. Copyright 2009 The Econometric Society.
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Bibliographic InfoArticle provided by Econometric Society in its journal Econometrica.
Volume (Year): 77 (2009)
Issue (Month): 3 (05)
Other versions of this item:
- SErgio Firpo & Nicole M. Fortin & Thomas Lemieux, 2006. "Unconditional Quantile Regressions," Textos para discussÃ£o 533, Department of Economics PUC-Rio (Brazil).
- Sergio Firpo & Nicole M. Fortin & Thomas Lemieux, 2007. "Unconditional Quantile Regressions," NBER Technical Working Papers 0339, National Bureau of Economic Research, Inc.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
- J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
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