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

  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},
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
Highly Influential
This paper has highly influenced 13 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS


Publications referenced by this paper.
Showing 1-10 of 29 references

Weak Convergence and Empirical Processes. With Applications to Statistics

  • A. W. Van der Vaart, J. A. Wellner
  • 1996
Highly Influential
5 Excerpts

Tests for Parameter Instability and Structural Change with Unknown

  • D.W.K. Andrews
  • Change Point,” Econometrica
  • 1993
Highly Influential
2 Excerpts

Integrated Public Use Microdata Series: Version 3.0. Minneapolis: Historical Census Project. University of Minesota

  • S. Ruggles, M. Sobek
  • 2003

Asymptotic Behavior of Regression Quantiles in Nonstationary, Dependent Cases

  • S. Portnoy
  • Journal of Multivariate Analysis 38,
  • 1991

Similar Papers

Loading similar papers…