02877cam a22003257 4500001000700000003000500007005001700012006001900029007001500048008004100063100002100104245015500125260006600280300005700346490004200403500001600445520148300461530006001944538007202004538003602076588002502112690010002137690006902237700002602306700002502332710004202357830007702399856003802476856003702514w10428NBER20200805153222.0m o d cr cnu||||||||200805s2004 mau fo 000 0 eng d1 aAngrist, Joshua.10aQuantile Regression under Misspecification, with an Application to the U.S. Wage Structure /cJoshua Angrist, Victor Chernozhukov, Ivan Fernandez-Val. aCambridge, Mass.bNational Bureau of Economic Researchc2004. a1 online resource:billustrations (black and white);1 aNBER working paper seriesvno. w10428 aApril 2004.3 aQuantile 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. aHardcopy version available to institutional subscribers aSystem requirements: Adobe [Acrobat] Reader required for PDF files. aMode of access: World Wide Web.0 aPrint version record 7aJ31 - Wage Level and Structure • Wage Differentials2Journal of Economic Literature class. 7aC13 - Estimation: General2Journal of Economic Literature class.1 aChernozhukov, Victor.1 aFernandez-Val, Ivan.2 aNational Bureau of Economic Research. 0aWorking Paper Series (National Bureau of Economic Research)vno. w10428.40uhttp://www.nber.org/papers/w1042840uhttp://dx.doi.org/10.3386/w10428