Hostname: page-component-74656f9884-c52kr Total loading time: 0 Render date: 2026-11-09T11:25:21.141Z Has data issue: false hasContentIssue false
  • English
  • Français

WHAT DO QUANTILE REGRESSIONS IDENTIFY FOR GENERAL STRUCTURAL FUNCTIONS?

Published online by Cambridge University Press:  02 October 2014

Yuya Sasaki
Yuya Sasaki*
Affiliation:
Johns Hopkins University
*
*Address correspondence to Yuya Sasaki, Johns Hopkins University, Department of Economics, 440 Mergenthaler Hall, 3400 N. Charles St., Baltimore, MD 21218, USA; e-mail: 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.

This paper shows what quantile regressions identify for general structural functions. Under fairly mild conditions, the quantile partial derivative identifies a weighted average of heterogeneous structural partial effects among the subpopulation of individuals at the conditional quantile of interest. This result justifies the use of quantile regressions as means of measuring heterogeneous causal effects for a general class of structural functions with multiple unobservables.

Information

Type
ARTICLES
Information
Econometric Theory , Volume 31 , Issue 5 , October 2015 , pp. 1102 - 1116
Copyright
Copyright © Cambridge University Press 2014 

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
Chernozhukov, V. & Hansen, C. (2005) An IV model of quantile treatment effects. Econometrica 73(1), 245–261.CrossRefGoogle Scholar
Chernozhukov, V., Imbens, G.W., & Newey, W.K. (2007) Instrumental variable estimation of nonseparable models. Journal of Econometrics 139(1), 4–14.CrossRefGoogle Scholar
Chesher, A. (2003) Identification in nonseparable models. Econometrica 71(5), 1405–1441.CrossRefGoogle Scholar
d’Haultfoeuille, X. & Février, P. (2011) Identification of Nonseparable Models with Endogeneity and Discrete Instruments. Working paper, CREST.Google Scholar
Heckman, J.J. (2007) The economics, technology, and neuroscience of human capability formation. Proceedings of the National Academy of Sciences 104(33), 13250–13255.CrossRefGoogle ScholarPubMed
Hoderlein, S. & Mammen, E. (2007) Identification of marginal effects in nonseparable models without monotonicity. Econometrica 75(5), 1513–1518.CrossRefGoogle 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
Lukeš, J. & Malý, J. (2005) Measure and Integration. Matfyz Press.Google Scholar
Padula, M. (2011) Asymptotic Stability of Steady Compressive Fluids. Springer.CrossRefGoogle Scholar
Rogers, C.A. & Falconer, K. (1998) Hausdorff Measures. Cambridge University Press.Google Scholar
Schennach, S.M. (2008) Quantile regression with mismeasured covariates. Econometric Theory 24(4), 1010–1043.CrossRefGoogle Scholar
Sen, A. (1985) A sociological approach to the measurement of poverty: A reply to professor Peter Townsend. Oxford Economic Papers 37(4), 669–676.CrossRefGoogle Scholar
Torgovitsky, A. (2011) Identification and Estimation of Nonparametric Quantile Regressions with Endogeneity. Working paper, Northwestern University.Google Scholar