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A New Instrumental Method for Dealing with Endogenous Selection

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  • Xavier d'Haultfoeuille

    (Crest)

Abstract

This paper develops a new method for dealing with endogenous selection. When selectionis directly driven by the dependent variable, the usual instrumental strategy based onthe independence between the outcome and the instruments is likely to fail. Instead, thearticle suggests to rely on independence between the instruments and the selection variable,conditional on the outcome. This may be particularly suitable for nonignorable nonresponse,binary models with missing covariates or Roy models with unobserved sector. Nonparametricidentification of the joint distribution of variables is obtained under completeness, arank condition which has been used recently in several nonparametric instrumental problems.Even if the conditional independence between the instrument and the selection fails,the approach provides sharp bounds on parameters of interest under weaker monotonicityconditions. Apart from identification, nonparametric and parametric estimation is also considered.Eventually, the method is applied to estimate the effect of grade retention in Frenchprimary schools.

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Bibliographic Info

Paper provided by Centre de Recherche en Economie et Statistique in its series Working Papers with number 2008-23.

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Length: 38
Date of creation: 2008
Date of revision:
Handle: RePEc:crs:wpaper:2008-23

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  1. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
  2. Adonis Yatchew, 1998. "Nonparametric Regression Techniques in Economics," Journal of Economic Literature, American Economic Association, vol. 36(2), pages 669-721, June.
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  6. Brian A. Jacob & Lars Lefgren, 2002. "Remedial Education and Student Achievement: A Regression-Discontinuity Analysis," NBER Working Papers 8918, National Bureau of Economic Research, Inc.
  7. Judith K. Hellerstein & Guido W. Imbens, 1999. "Imposing Moment Restrictions From Auxiliary Data By Weighting," The Review of Economics and Statistics, MIT Press, vol. 81(1), pages 1-14, February.
  8. Charles F. Manski & John V. Pepper, 1998. "Monotone Instrumental Variables with an Application to the Returns to Schooling," NBER Technical Working Papers 0224, National Bureau of Economic Research, Inc.
  9. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects and Econometric Policy Evaluation," NBER Working Papers 11259, National Bureau of Economic Research, Inc.
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  12. Xiaohong Chen & Yingyao Hu, 2006. "Identification and Inference of Nonlinear Models Using Two Samples with Arbitrary Measurement Errors," Cowles Foundation Discussion Papers 1590, Cowles Foundation for Research in Economics, Yale University.
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  16. Guido Imbens, 2000. "Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score," Econometric Society World Congress 2000 Contributed Papers 1166, Econometric Society.
  17. Richard Blundell & Xiaohong Chen & Dennis Kristensen, 2007. "Semi-Nonparametric IV Estimation of Shape-Invariant Engel Curves," Econometrica, Econometric Society, vol. 75(6), pages 1613-1669, November.
  18. Yingyao Hu & Susanne M. Schennach, 2008. "Instrumental Variable Treatment of Nonclassical Measurement Error Models," Econometrica, Econometric Society, vol. 76(1), pages 195-216, 01.
  19. Whitney K. Newey & James L. Powell, 2003. "Instrumental Variable Estimation of Nonparametric Models," Econometrica, Econometric Society, vol. 71(5), pages 1565-1578, 09.
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  23. P. Gagliardini & O. Scaillet, 2006. "Tikhonov Regularization for Functional Minimum Distance Estimators," Swiss Finance Institute Research Paper Series 06-30, Swiss Finance Institute, revised Nov 2006.
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Cited by:
  1. d'Haultfoeuille, Xavier & Maurel, Arnaud, 2009. "Inference on a Generalized Roy Model, with an Application to Schooling Decisions in France," IZA Discussion Papers 4606, Institute for the Study of Labor (IZA).

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