Hostname: page-component-77f85d65b8-g4pgd Total loading time: 0 Render date: 2026-03-29T13:50:25.032Z Has data issue: false hasContentIssue false
  • English
  • Français

ON USING LINEAR QUANTILE REGRESSIONS FOR CAUSAL INFERENCE

Published online by Cambridge University Press:  23 May 2016

Ryutah Kato  and
Yuya Sasaki
Ryutah Kato*
Affiliation:
Johns Hopkins
Yuya Sasaki*
Affiliation:
Johns Hopkins
*
*Address correspondence to Ryutah Kato and Yuya Sasaki, Johns Hopkins University, Department of Economics, Wyman Park Building 544E, 3400 N. Charles St., Baltimore, MD 21218, USA; e-mail: rkato3@jhu.edu and sasaki@jhu.edu.
*Address correspondence to Ryutah Kato and Yuya Sasaki, Johns Hopkins University, Department of Economics, Wyman Park Building 544E, 3400 N. Charles St., Baltimore, MD 21218, USA; e-mail: rkato3@jhu.edu and sasaki@jhu.edu.
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the 'Save PDF' action button.

We show that the slope parameter of the linear quantile regression measures a weighted average of the local slopes of the conditional quantile function. Extending this result, we also show that the slope parameter measures a weighted average of the partial effects for a general structural function. Our results support the use of linear quantile regressions for causal inference in the presence of nonlinearity and multivariate unobserved heterogeneity. The same conclusion applies to linear regressions.

Information

Type
ARTICLES
Information
Econometric Theory , Volume 33 , Issue 3 , June 2017 , pp. 664 - 690
Copyright
Copyright © Cambridge University Press 2016 

Footnotes

We were benefited from useful suggestions from V. Chernozhukov, I. Fernández-Val, and K. Kato. All the remaining errors are ours.

References

REFERENCES

Angrist, J., Chernozhukov, V., & Fernández-Val, I. (2006) Quantile regression under misspecification, with an application to the U.S. wage structure. Econometrica 74(2), 539–563.CrossRefGoogle Scholar
Angrist, J.D. & Imbens, G.W. (1995) Two-stage least squares estimation of average causal effects in models with variable treatment intensity. Journal of the American Statistical Association 90(430), 431–442.CrossRefGoogle Scholar
Angrist, J.D. & Krueger, A.B. (1999) Empirical strategies in labor economics. In Ashenfelter, O.C. and Card, D. (eds.), Handbook of Labor Economics, Vol. 3, Part A, 1277–1366.CrossRefGoogle Scholar
Chamberlain, G. (1984) Panel data. In Griliches, Z. and Intriligator, M. (eds.), Handbook of Econometrics, Vol. 2, 1247–1318.CrossRefGoogle Scholar
Chernozhukov, V., Fernández-Val, I., & Galichon, A. (2010) Quantile and probability curves without crossing. Econometrica 74(2), 539–563.Google Scholar
Chernozhukov, V., Fernández-Val, I., Hoderlein, S., Holzmann, H., & Newey, W. (2015) Nonparametric identification in panels using quantiles. Journal of Econometrics 188(2), 378–392.Google Scholar
Chernozhukov, V. & Hansen, C. (2005) An IV model of quantile treatment effects. Econometrica 73(1), 245–261.CrossRefGoogle Scholar
Chernozhukov, V. & Hansen, C. (2013) Quantile models with endogeneity. Annual Review of Economics 5(1), 57–81.CrossRefGoogle Scholar
Goldberger, A.S. (1991) A Course in Econometrics. Harvard University Press.Google Scholar
Imbens, G.W. & Newey, W.K. (2009) Identification and estimation of triangular simultaneous equations models without additivity. Econometrica 77(5), 1481–1512.Google Scholar
Koenker, R. (2005) Quantile Regression. Cambridge University Press.CrossRefGoogle Scholar
Koenker, R. & Bassett, G. Jr. (1978) Regression quantiles. Econometrica 46(1), 33–50.CrossRefGoogle Scholar
Koenker, R. & Hallock, K.F. (2001) Quantile regression. Journal of Economic Perspectives 15(4), 143–156.CrossRefGoogle Scholar
Lee, Y.-Y. (2013) Interpretation and semiparametric efficiency in quantile regression under misspecification. Working Paper, University of Wisconsin-Madison.Google Scholar
Sasaki, Y. (2015) What do quantile regressions identify for general structural functions? Econometric Theory 31(5), 1102–1116.CrossRefGoogle Scholar
White, H. (1980) Using least squares to approximate unknown regression functions. International Economic Review 21(1), 149–170.CrossRefGoogle Scholar
Yitzhaki, S. (1996) On using linear regressions in welfare economics. Journal of Business and Economic Statistics 14 (4), 478–486.Google Scholar