IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2603.13505.html

Testing the Exclusion Restriction in IV Models Using Non-Gaussianity: A LiNGAM-Based Approach

Author

Listed:
  • Fernando Delbianco

Abstract

Instrumental variable (IV) methods rely critically on the exclusion restriction, which is untestable in exactly-identified models under standard assumptions. We propose a framework combining IV analysis with the LiNGAM method to test this restriction by exploiting non-Gaussianity in the data. Under non-Gaussian structural errors, the exclusion violation parameter is point-identified without additional instruments. Five complementary tests (bootstrap percentile, asymptotic normal, permutation, likelihood ratio, and independence-based) are introduced to assess the restriction under varying data conditions. Monte Carlo simulations and an empirical application to the Card (1995) dataset demonstrate controlled Type I error rates and reasonable power against economically relevant violations.

Suggested Citation

  • Fernando Delbianco, 2026. "Testing the Exclusion Restriction in IV Models Using Non-Gaussianity: A LiNGAM-Based Approach," Papers 2603.13505, arXiv.org.
    • Handle: RePEc:arx:papers:2603.13505
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2603.13505
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Berkowitz, Daniel & Caner, Mehmet & Fang, Ying, 2012. "The validity of instruments revisited," Journal of Econometrics, Elsevier, vol. 166(2), pages 255-266.
    2. James H. Stock & Motohiro Yogo, 2002. "Testing for Weak Instruments in Linear IV Regression," NBER Technical Working Papers 0284, National Bureau of Economic Research, Inc.
    3. Dieterle, Steven G. & Snell, Andy, 2016. "A simple diagnostic to investigate instrument validity and heterogeneous effects when using a single instrument," Labour Economics, Elsevier, vol. 42(C), pages 76-86.
    4. Susan Athey & Guido W. Imbens, 2017. "The State of Applied Econometrics: Causality and Policy Evaluation," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 3-32, Spring.
    5. Thorsten Thadewald & Herbert Buning, 2007. "Jarque-Bera Test and its Competitors for Testing Normality - A Power Comparison," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(1), pages 87-105.
    6. Marco Ventura, 2018. "Testing the validity of instruments in an exactly identified equation," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 8(2), pages 159-169.
    7. Whitney K. Newey & James L. Powell, 2003. "Instrumental Variable Estimation of Nonparametric Models," Econometrica, Econometric Society, vol. 71(5), pages 1565-1578, September.
    8. Toru Kitagawa, 2015. "A Test for Instrument Validity," Econometrica, Econometric Society, vol. 83(5), pages 2043-2063, September.
    9. Aviv Nevo & Adam M. Rosen, 2012. "Identification With Imperfect Instruments," The Review of Economics and Statistics, MIT Press, vol. 94(3), pages 659-671, August.
    10. Paul Hünermund & Elias Bareinboim, 2025. "Causal inference and data fusion in econometrics," The Econometrics Journal, Royal Economic Society, vol. 28(1), pages 41-82.
    11. Ismael Mourifié & Yuanyuan Wan, 2017. "Testing Local Average Treatment Effect Assumptions," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 305-313, May.
    12. Imbens, Guido W & Angrist, Joshua D, 1994. "Identification and Estimation of Local Average Treatment Effects," Econometrica, Econometric Society, vol. 62(2), pages 467-475, March.
    Full references (including those not matched with items on IDEAS)

    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. Arne Henningsen & Guy Low & David Wuepper & Tobias Dalhaus & Hugo Storm & Dagim Belay & Stefan Hirsch, 2024. "Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists," IFRO Working Paper 2024/03, University of Copenhagen, Department of Food and Resource Economics.
    2. Sun, Zhenting & Wüthrich, Kaspar, 2025. "Pairwise valid instruments," Journal of Econometrics, Elsevier, vol. 250(C).
    3. Rui Wang, 2023. "Point Identification of LATE with Two Imperfect Instruments," Papers 2303.13795, arXiv.org.
    4. Wang, Xintong & Flores, Carlos A. & Flores-Lagunes, Alfonso, 2025. "The effects of Vietnam-era military service on the long-term health of veterans: A bounds analysis," Journal of Health Economics, Elsevier, vol. 101(C).
    5. Leonard Goff, 2024. "When does IV identification not restrict outcomes?," Papers 2406.02835, arXiv.org, revised Feb 2025.
    6. Seojeong Lee, 2018. "A Consistent Variance Estimator for 2SLS When Instruments Identify Different LATEs," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 36(3), pages 400-410, July.
    7. Kédagni, Désiré, 2023. "Identifying treatment effects in the presence of confounded types," Journal of Econometrics, Elsevier, vol. 234(2), pages 479-511.
    8. Andrew E Clark & Rong Zhu, 2024. "Taking Back Control? Quasi-Experimental Evidence on the Impact of Retirement on Locus of Control," The Economic Journal, Royal Economic Society, vol. 134(660), pages 1465-1493.
    9. Kitagawa, Toru, 2021. "The identification region of the potential outcome distributions under instrument independence," Journal of Econometrics, Elsevier, vol. 225(2), pages 231-253.
    10. Stefan Tübbicke, 2023. "When to use matching and weighting or regression in instrumental variable estimation? Evidence from college proximity and returns to college," Empirical Economics, Springer, vol. 65(6), pages 2979-2999, December.
    11. Yoichi Arai & Yu‐Chin Hsu & Toru Kitagawa & Ismael Mourifié & Yuanyuan Wan, 2022. "Testing identifying assumptions in fuzzy regression discontinuity designs," Quantitative Economics, Econometric Society, vol. 13(1), pages 1-28, January.
    12. Mourifié, Ismael & Wan, Yuanyuan, 2025. "Layered policy analysis in program evaluation using the marginal treatment effect," Journal of Econometrics, Elsevier, vol. 251(C).
    13. Xiaolin Sun, 2022. "Estimation of Heterogeneous Treatment Effects Using a Conditional Moment Based Approach," Papers 2210.15829, arXiv.org, revised Oct 2024.
    14. Jan Priebe, 2020. "Quasi-experimental evidence for the causal link between fertility and subjective well-being," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(3), pages 839-882, July.
    15. Alexeev, Sergey & Weatherburn, Don, 2022. "Fines for illicit drug use do not prevent future crime: evidence from randomly assigned judges," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 555-575.
    16. Domenico Depalo, 2020. "Explaining the causal effect of adherence to medication on cholesterol through the marginal patient," Health Economics, John Wiley & Sons, Ltd., vol. 29(S1), pages 110-126, October.
    17. Nadja van 't Hoff, 2023. "Identifying Causal Effects of Discrete, Ordered and ContinuousTreatments using Multiple Instrumental Variables," Papers 2311.17575, arXiv.org, revised Oct 2024.
    18. Bhuller, Manudeep & Sigstad, Henrik, 2024. "2SLS with multiple treatments," Journal of Econometrics, Elsevier, vol. 242(1).
    19. Sun, Zhenting, 2023. "Instrument validity for heterogeneous causal effects," Journal of Econometrics, Elsevier, vol. 237(2).
    20. Xintong Wang & Alfonso Flores-Lagunes, 2022. "Conscription and Military Service: Do They Result in Future Violent and Nonviolent Incarcerations and Recidivism?," Journal of Human Resources, University of Wisconsin Press, vol. 57(5), pages 1715-1757.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2603.13505. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.