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ANOTHER LOOK AT THE IDENTIFICATION AT INFINITY OF SAMPLE SELECTION MODELS

Published online by Cambridge University Press:  06 July 2012

Xavier D’Haultfoeuille  and
Arnaud Maurel
Xavier D’Haultfoeuille*
Affiliation:
CREST
Arnaud Maurel
Affiliation:
Duke University
*
*Address correspondence to Xavier D’Haultfoeuille, CREST, 15 boulevard Gabriel Péri, 92 240 Malakoff, France; e-mail: [email protected].
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Abstract

It is often believed that without instruments, endogenous sample selection models are identified only if a covariate with a large support is available (see, e.g., Chamberlain, 1986, Journal of Econometrics 32, 189–218; Lewbel, 2007, Journal of Econometrics141, 777–806) . We propose a new identification strategy mainly based on the condition that the selection variable becomes independent of the covariates for large values of the outcome. No large support on the covariates is required. Moreover, we prove that this condition is testable. We finally show that our strategy can be applied to the identification of generalized Roy models.

Type
MISCELLANEA
Information
Econometric Theory , Volume 29 , Issue 1 , February 2013 , pp. 213 - 224
Copyright
Copyright © Cambridge University Press 2012 

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Footnotes

We are grateful to Magali Beffy, Edwin Leuven, and Arthur Lewbel for helpful comments. We also thank the editor Yuichi Kitamura and two anonymous referees for their valuable remarks and suggestions.

References

REFERENCES

Abbring, J. (2010) Identification of dynamic discrete choice models. Annual Review of Economics 2, 367–394.Google Scholar
Abbring, J. & van den Berg, G. (2003) The identifiability of the mixed proportional hazards competing risks model. Journal of the Royal Statistical Society, Series B 65, 701–710.CrossRefGoogle Scholar
Andrews, D.K. & Schafgans, M. (1998) Semiparametric estimation of the intercept of a sample selection model. Review of Economic Studies 65, 497–517.Google Scholar
Bayer, P.J., Khan, S., & Timmins, C. (2011) Nonparametric identification and estimation in a Roy model with common nonpecuniary returns. Journal of Business & Economic Statistics 29, 201–215.Google Scholar
Borjas, G. (1987) Self-selection and the earnings of immigrants. American Economic Review 77,531–553.Google Scholar
Carneiro, P., Hansen, K., & Heckman, J. (2003) Estimating distributions of treatment effects with an application to the returns to schooling and measurement of the effects of uncertainty on college choice. International Economic Review 44, 361–422.Google Scholar
Chamberlain, G. (1986) Asymptotic efficiency in semiparametric model with censoring. Journal of Econometrics 32, 189–218.CrossRefGoogle Scholar
Dagsvik, J. & Strøm, S. (2006) Sectoral labour supply, choice restrictions and functional form. Journal of Applied Econometrics 21, 803–826.CrossRefGoogle Scholar
D’Haultfœuille, X. & Maurel, A. (2011) Inference on an Extended Roy Model, with an Application to Schooling Decisions in France. Working paper, Duke University.Google Scholar
Elbers, C. & Ridder, G. (1982) True and spurious duration dependence The identifiability of the proportional hazard model. Review of Economic Studies 49, 403–409.Google Scholar
Heckman, J.J. (1974) Shadow prices, market wages, and labor supply. Econometrica 42, 679–694.Google Scholar
Heckman, J.J. (1990) Varieties of selection bias. American Economic Review 80, 313–318.Google Scholar
Heckman, J.J. & Honore, B. (1989) The identifiability of competing risks models. Biometrika 76, 325–330.CrossRefGoogle Scholar
Heckman, J. & Vytlacil, E. (2005) Structural equations, treatment effects, and econometric policy evaluation. Econometrica 73, 669–738.CrossRefGoogle Scholar
Khan, S. & Tamer, E. (2010) Irregular identification, support conditions and inverse weight estimation. Econometrica 78, 2021–2042.Google Scholar
Lee, S. (2006) Identification of a competing risks model with unknown transformations of latent failure times. Biometrika 93, 996–1002.CrossRefGoogle Scholar
Lee, S. & Lewbel, A. (2011) Nonparametric Identification of Accelerated Failure Time Competing Risks Models. Working paper, Boston College.Google Scholar
Lewbel, A. (2007) Endogenous selection or treatment model estimation. Journal of Econometrics 141, 777–806.CrossRefGoogle Scholar
Roy, A.D. (1951) Some thoughts on the distribution of earnings. Oxford Economic Papers (New Series) 3, 135–146.CrossRefGoogle Scholar
Schafgans, M. & Zinde-Walsh, V. (2002) On intercept estimation in the sample selection model. Econometric Theory 18, 40–50.CrossRefGoogle Scholar
Vella, F. (1998) Estimating models with sample selection bias: A survey. Journal of Human Resources 33, 127–169.CrossRefGoogle Scholar
Willis, R. & Rosen, S. (1979) Education and self-selection. Journal of Political Economy 87, S7–S36.CrossRefGoogle Scholar