Quantile Regression under Misspecification , with an Application to the U . S . Wage Structure

@inproceedings{Angrist2004QuantileRU,
  title={Quantile Regression under Misspecification , with an Application to the U . S . Wage Structure},
  author={Joshua Angrist and Victor Chernozhukov and Iv{\'a}n Fern{\'a}ndez-Val},
  year={2004}
}
Quantile regression (QR) fits a linear model for conditional quantiles, just as ordinary least squares (OLS) fits a linear model for conditional means. An attractive feature of OLS is that it gives the minimum mean square error linear approximation to the conditional expectation function even when the linear model is misspecified. Empirical research using quantile regression with discrete covariates suggests that QR may have a similar property, but the exact nature of the linear approximation… CONTINUE READING
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