IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v200y2021ics0165176521000458.html

On the treatment effects of a binary choice outcome model

Author

Listed:
  • Hasebe, Takuya

Abstract

This paper discusses an econometric model to estimate treatment effects for binary choice outcomes. We use a copula to model the dependence of unobservable terms. The copula-based approach allows for various dependence structures. A simulation study shows the misspecification of the dependence structures results in biased estimation of the treatment effects.

Suggested Citation

  • Hasebe, Takuya, 2021. "On the treatment effects of a binary choice outcome model," Economics Letters, Elsevier, vol. 200(C).
    • Handle: RePEc:eee:ecolet:v:200:y:2021:i:c:s0165176521000458
    DOI: 10.1016/j.econlet.2021.109768
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176521000458
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2021.109768?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. James J. Heckman & Vytlacil, Edward J., 2007. "Econometric Evaluation of Social Programs, Part II: Using the Marginal Treatment Effect to Organize Alternative Econometric Estimators to Evaluate Social Programs, and to Forecast their Effects in New Environments," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 71, Elsevier.
    2. James J. Heckman & Edward Vytlacil, 2005. "Structural Equations, Treatment Effects, and Econometric Policy Evaluation," Econometrica, Econometric Society, vol. 73(3), pages 669-738, May.
    3. Trivedi, Pravin K. & Zimmer, David M., 2007. "Copula Modeling: An Introduction for Practitioners," Foundations and Trends(R) in Econometrics, now publishers, vol. 1(1), pages 1-111, April.
    4. Aakvik, Arild & Heckman, James J. & Vytlacil, Edward J., 2005. "Estimating treatment effects for discrete outcomes when responses to treatment vary: an application to Norwegian vocational rehabilitation programs," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 15-51.
    5. Hasebe, Takuya, 2013. "Marginal effects of a bivariate binary choice model," Economics Letters, Elsevier, vol. 121(2), pages 298-301.
    6. Rainer Winkelmann, 2012. "Copula Bivariate Probit Models: With An Application To Medical Expenditures," Health Economics, John Wiley & Sons, Ltd., vol. 21(12), pages 1444-1455, December.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. Tatiana Rodionova, 2024. "Estimating the effect of satisfaction with working conditions on employee health," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 76, pages 70-95.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tatiana Rodionova, 2024. "Estimating the effect of satisfaction with working conditions on employee health," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 76, pages 70-95.
    2. Cornelissen, Thomas & Dustmann, Christian & Raute, Anna & Schönberg, Uta, 2016. "From LATE to MTE: Alternative methods for the evaluation of policy interventions," Labour Economics, Elsevier, vol. 41(C), pages 47-60.
    3. Thomas Cornelissen & Christian Dustmann & Anna Raute & Uta Schönberg, 2018. "Who Benefits from Universal Child Care? Estimating Marginal Returns to Early Child Care Attendance," Journal of Political Economy, University of Chicago Press, vol. 126(6), pages 2356-2409.
    4. Philipp Eisenhauer & James J. Heckman & Edward Vytlacil, 2015. "The Generalized Roy Model and the Cost-Benefit Analysis of Social Programs," Journal of Political Economy, University of Chicago Press, vol. 123(2), pages 413-443.
    5. Heckman, James J. & Urzúa, Sergio, 2010. "Comparing IV with structural models: What simple IV can and cannot identify," Journal of Econometrics, Elsevier, vol. 156(1), pages 27-37, May.
    6. repec:hum:wpaper:sfb649dp2012-042 is not listed on IDEAS
    7. Jörg Schwiebert, 2016. "Multinomial choice models based on Archimedean copulas," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 100(3), pages 333-354, July.
    8. Sloczynski, Tymon, 2018. "A General Weighted Average Representation of the Ordinary and Two-Stage Least Squares Estimands," IZA Discussion Papers 11866, IZA Network @ LISER.
    9. Cunha, Flavio & Heckman, James, 2008. "A New Framework For The Analysis Of Inequality," Macroeconomic Dynamics, Cambridge University Press, vol. 12(S2), pages 315-354, September.
    10. Moffitt, Robert A. & Zahn, Matthew V., 2022. "The Marginal Labor Supply Disincentives of Welfare: Evidence from Administrative Barriers to Participation," Economics Working Paper Archive 66674, The Johns Hopkins University,Department of Economics.
    11. Amanda E. Kowalski, 2018. "Extrapolation using Selection and Moral Hazard Heterogeneity from within the Oregon Health Insurance Experiment," Cowles Foundation Discussion Papers 2135, Cowles Foundation for Research in Economics, Yale University.
    12. Stefan Hoderlein & Yuya Sasaki, 2013. "Outcome Conditioned Treatment Effects," Boston College Working Papers in Economics 840, Boston College Department of Economics.
    13. Pereda-Fernández, Santiago, 2023. "Identification and estimation of triangular models with a binary treatment," Journal of Econometrics, Elsevier, vol. 234(2), pages 585-623.
    14. Huber Martin & Wüthrich Kaspar, 2019. "Local Average and Quantile Treatment Effects Under Endogeneity: A Review," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-27, January.
    15. H. Evans & A. Basu, 2011. "Exploring comparative effect heterogeneity with instrumental variables: prehospital intubation and mortality," Health, Econometrics and Data Group (HEDG) Working Papers 11/26, HEDG, c/o Department of Economics, University of York.
    16. Pedro Carneiro & James J. Heckman & Edward J. Vytlacil, 2011. "Estimating Marginal Returns to Education," American Economic Review, American Economic Association, vol. 101(6), pages 2754-2781, October.
    17. Fan, Yanqin & Guerre, Emmanuel & Zhu, Dongming, 2017. "Partial identification of functionals of the joint distribution of “potential outcomes”," Journal of Econometrics, Elsevier, vol. 197(1), pages 42-59.
    18. Gong, Jie & Lu, Yi & Xie, Huihua, 2020. "The average and distributional effects of teenage adversity on long-term health," Journal of Health Economics, Elsevier, vol. 71(C).
    19. Rahul Singh & Liyuan Xu & Arthur Gretton, 2020. "Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves," Papers 2010.04855, arXiv.org, revised Oct 2022.
    20. Heckman, James J. & Schmierer, Daniel & Urzua, Sergio, 2010. "Testing the correlated random coefficient model," Journal of Econometrics, Elsevier, vol. 158(2), pages 177-203, October.
    21. Mammen, Enno & Rothe, Christoph & Schienle, Melanie, 2012. "Generated covariates in nonparametric estimation: A short review," SFB 649 Discussion Papers 2012-042, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.

    More about this item

    Keywords

    ;
    ;
    ;

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

    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:eee:ecolet:v:200:y:2021:i:c:s0165176521000458. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    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.