IDEAS home Printed from https://ideas.repec.org/p/duk/dukeec/11-11.html

Another Look at the Identification at Infinity of Sample Selection Models

Author

Listed:
  • Arnaud Maurel
  • Xavier D'Haultfoeuille

Abstract

It is often believed that without instrument, endogenous sample selection models are identified only if a covariate with a large support is available (see, e.g., Chamberlain, 1986, and Lewbel, 2007). 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.

Suggested Citation

  • Arnaud Maurel & Xavier D'Haultfoeuille, 2011. "Another Look at the Identification at Infinity of Sample Selection Models," Working Papers 11-11, Duke University, Department of Economics.
    • Handle: RePEc:duk:dukeec:11-11
    as

    Download full text from publisher

    File URL: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1881049
    File Function: main text
    Download Restriction: no
    ---><---

    Other versions of this item:

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. D’Haultfœuille, Xavier & Maurel, Arnaud, 2013. "Inference on an extended Roy model, with an application to schooling decisions in France," Journal of Econometrics, Elsevier, vol. 174(2), pages 95-106.
    2. D’Haultfœuille, Xavier & Maurel, Arnaud & Zhang, Yichong, 2018. "Extremal quantile regressions for selection models and the black–white wage gap," Journal of Econometrics, Elsevier, vol. 203(1), pages 129-142.
    3. 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 of Labor Economics (IZA).
    4. Zhewen Pan & Zhengxin Wang & Junsen Zhang & Yahong Zhou, 2024. "Marginal treatment effects in the absence of instrumental variables," Papers 2401.17595, arXiv.org, revised Aug 2024.
    5. Breunig, Christoph & Mammen, Enno & Simoni, Anna, 2018. "Nonparametric estimation in case of endogenous selection," Journal of Econometrics, Elsevier, vol. 202(2), pages 268-285.
    6. Fan Wu & Yi Xin, 2024. "Estimating Nonseparable Selection Models: A Functional Contraction Approach," Papers 2411.01799, arXiv.org, revised Dec 2025.
    7. Ismaël Mourifié & Marc Henry & Romuald Méango, 2020. "Sharp Bounds and Testability of a Roy Model of STEM Major Choices," Journal of Political Economy, University of Chicago Press, vol. 128(8), pages 3220-3283.
    8. Xavier D’Haultfoeuille & Arnaud Maurel & Xiaoyun Qiu & Yichong Zhang, 2020. "Estimating selection models without an instrument with Stata," Stata Journal, StataCorp LLC, vol. 20(2), pages 297-308, June.
    9. repec:hum:wpaper:sfb649dp2015-050 is not listed on IDEAS
    10. Arulampalam, Wiji & Corradi, Valentina & Gutknecht, Daniel, 2024. "Intercept Estimation In Nonlinear Selection Models," Econometric Theory, Cambridge University Press, vol. 40(6), pages 1311-1363, December.
    11. Jochmans, Koen & Henry, Marc & Salanié, Bernard, 2017. "Inference On Two-Component Mixtures Under Tail Restrictions," Econometric Theory, Cambridge University Press, vol. 33(3), pages 610-635, June.
    12. Dylan Brewer & Alyssa Carlson, 2024. "Addressing sample selection bias for machine learning methods," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 39(3), pages 383-400, April.
    13. Zhewen Pan & Yifan Zhang, 2024. "Locally robust semiparametric estimation of sample selection models without exclusion restrictions," Papers 2412.01208, arXiv.org.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:duk:dukeec:11-11. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Department of Economics Webmaster (email available below). General contact details of provider: http://econ.duke.edu/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.