Unconditional Quantile Regression for Exogenous or Endogenous Treatment Variables
AbstractThis paper introduces an unconditional quantile regression (UQR) estimator that can be used for exogenous or endogenous treatment variables. Traditional quantile estimators provide conditional treatment effects. Typically, we are interested in unconditional quantiles, characterizing the distribution of the outcome variable for different values of the treatment variables. Conditioning on additional covariates, however, may be necessary for identification of these treatment effects. With conditional quantile models, the inclusion of additional covariates changes the interpretation of the estimates. The UQR and IV-UQR estimators allow for one to condition on covariates without altering the interpretation. This estimator is a more general version of traditional quantile estimators.
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Bibliographic InfoPaper provided by RAND Corporation Publications Department in its series Working Papers with number 824.
Length: 33 pages
Date of creation: Jan 2011
Date of revision:
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unconditional quantile treatment effects; quantile regression; instrumental variables; identification;
Find related papers by JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
- C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
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