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

54 Pages Posted: 26 Apr 2004 Last revised: 1 Jul 2021

See all articles by Joshua D. Angrist

Joshua D. Angrist

Massachusetts Institute of Technology (MIT) - Department of Economics; National Bureau of Economic Research (NBER); IZA Institute of Labor Economics

Victor Chernozhukov

Massachusetts Institute of Technology (MIT) - Department of Economics

Iván Fernández‐Val

Boston University - Department of Economics

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Date Written: April 2004

Abstract

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 has remained elusive. In this paper, we show that QR can be interpreted as minimizing a weighted mean-squared error loss function for specification error. The weighting function is an average density of the dependent variable near the true conditional quantile. The weighted least squares interpretation of QR is used to derive an omitted variables bias formula and a partial quantile correlation concept, similar to the relationship between partial correlation and OLS. We also derive general asymptotic results for QR processes allowing for misspecification of the conditional quantile function, extending earlier results from a single quantile to the entire process. The approximation properties of QR are illustrated through an analysis of the wage structure and residual inequality in US Census data for 1980, 1990, and 2000. The results suggest continued residual inequality growth in the 1990s, primarily in the upper half of the wage distribution and for college graduates.

Suggested Citation

Angrist, Joshua and Chernozhukov, Victor and Fernandez-Val, Ivan, Quantile Regression Under Misspecification, with an Application to the U.S. Wage Structure (April 2004). NBER Working Paper No. w10428, Available at SSRN: https://ssrn.com/abstract=529008

Joshua Angrist (Contact Author)

Massachusetts Institute of Technology (MIT) - Department of Economics ( email )

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Victor Chernozhukov

Massachusetts Institute of Technology (MIT) - Department of Economics ( email )

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Ivan Fernandez-Val

Boston University - Department of Economics ( email )

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HOME PAGE: http://people.mit.edu/ivanf

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