IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2308.10138.html
   My bibliography  Save this paper

Genuinely Robust Inference for Clustered Data

Author

Listed:
  • Harold D. Chiang
  • Yuya Sasaki
  • Yulong Wang

Abstract

Conventional cluster-robust inference methods are inconsistent when clusters are unignorably large. We derive a necessary and sufficient condition for consistency, which is violated in 77% of empirical studies published in American Economic Review and Econometrica (2020-2021). To address this, we propose two methods: (i) score subsampling, which retains the original estimator, and (ii) size-adjusted reweighting, which is easy to implement in software like Stata and remains valid if the cluster size follows Zipf's law. Simulations confirm the reliability and uniform size control of these approaches, offering robust alternatives where conventional methods fail.

Suggested Citation

  • Harold D. Chiang & Yuya Sasaki & Yulong Wang, 2023. "Genuinely Robust Inference for Clustered Data," Papers 2308.10138, arXiv.org, revised Jan 2025.
    • Handle: RePEc:arx:papers:2308.10138
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2308.10138
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Davidson, James, 1994. "Stochastic Limit Theory: An Introduction for Econometricians," OUP Catalogue, Oxford University Press, number 9780198774037.
    2. Nicholas M. Kiefer & Timothy J. Vogelsang, 2002. "Heteroskedasticity-Autocorrelation Robust Standard Errors Using The Bartlett Kernel Without Truncation," Econometrica, Econometric Society, vol. 70(5), pages 2093-2095, September.
    3. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    4. Yuya Sasaki & Yulong Wang, 2022. "Non-Robustness of the Cluster-Robust Inference: with a Proposal of a New Robust Method," Papers 2210.16991, arXiv.org, revised Jan 2025.
    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. Koo, Bonsoo & Seo, Myung Hwan, 2015. "Structural-break models under mis-specification: Implications for forecasting," Journal of Econometrics, Elsevier, vol. 188(1), pages 166-181.
    2. Matei Demetrescu & Christoph Hanck & Robinson Kruse‐Becher, 2022. "Robust inference under time‐varying volatility: A real‐time evaluation of professional forecasters," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 1010-1030, August.
    3. Xu, Ke-Li, 2012. "Robustifying multivariate trend tests to nonstationary volatility," Journal of Econometrics, Elsevier, vol. 169(2), pages 147-154.
    4. Wenjie Wang & Yichong Zhang, 2021. "Wild Bootstrap for Instrumental Variables Regressions with Weak and Few Clusters," Papers 2108.13707, arXiv.org, revised Jan 2024.
    5. Lui, Yiu Lim & Phillips, Peter C.B. & Yu, Jun, 2024. "Robust testing for explosive behavior with strongly dependent errors," Journal of Econometrics, Elsevier, vol. 238(2).
    6. Sun, Yixiao, 2014. "Let’s fix it: Fixed-b asymptotics versus small-b asymptotics in heteroskedasticity and autocorrelation robust inference," Journal of Econometrics, Elsevier, vol. 178(P3), pages 659-677.
    7. Cheol-Keun Cho & Timothy J. Vogelsang, 2016. "Fixed- b Inference for Testing Structural Change in a Time Series Regression," Econometrics, MDPI, vol. 5(1), pages 1-26, December.
    8. Li, Kunpeng & Cui, Guowei & Lu, Lina, 2020. "Efficient estimation of heterogeneous coefficients in panel data models with common shocks," Journal of Econometrics, Elsevier, vol. 216(2), pages 327-353.
    9. Jen-Je Su, 2004. "Testing for no autocorrelation using a modified Lobato test," Economics Bulletin, AccessEcon, vol. 3(46), pages 1-9.
    10. Miao Wang & Hong Zhuang, 2022. "Effect of official development assistance on adolescent fertility rate: within-country evidence," Economics Bulletin, AccessEcon, vol. 42(2), pages 566-590.
    11. Schwartz, Joseph A. & Allen, Samantha L., 2024. "The accumulated impact of direct and indirect workplace violence exposure on mental health and physiological activity among correctional officers," Journal of Criminal Justice, Elsevier, vol. 93(C).
    12. Paulo M. D. C. Parente & Richard J. Smith, 2021. "Quasi‐maximum likelihood and the kernel block bootstrap for nonlinear dynamic models," Journal of Time Series Analysis, Wiley Blackwell, vol. 42(4), pages 377-405, July.
    13. Jongmoo Jay Choi & Hoje Jo & Jimi Kim & Moo Sung Kim, 2018. "Business Groups and Corporate Social Responsibility," Journal of Business Ethics, Springer, vol. 153(4), pages 931-954, December.
    14. Clément de Chaisemartin & Jaime Ramirez-Cuellar, 2024. "At What Level Should One Cluster Standard Errors in Paired and Small-Strata Experiments?," American Economic Journal: Applied Economics, American Economic Association, vol. 16(1), pages 193-212, January.
    15. Francesca Carta & Lucia Rizzica, 2015. "Female employment and pre-kindergarten: on the uninteded effects of an Italian reform," Temi di discussione (Economic working papers) 1030, Bank of Italy, Economic Research and International Relations Area.
    16. Rotunno, Lorenzo, 2016. "Political stability and trade agreements: Evidence for ‘endgame FTAs’," European Journal of Political Economy, Elsevier, vol. 45(C), pages 133-148.
    17. Yuheng Lin & Dooruj Rambaccussing & Yu Zhu, 2024. "The impact of international students in the UK on the cultural goods trade," French Stata Users' Group Meetings 2024 29, Stata Users Group.
    18. Friedrich, Sarah & Pauly, Markus, 2018. "MATS: Inference for potentially singular and heteroscedastic MANOVA," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 166-179.
    19. Nora Gordon & Sarah Reber, 2018. "The effects of school desegregation on mixed-race births," Journal of Population Economics, Springer;European Society for Population Economics, vol. 31(2), pages 561-596, April.
    20. Borisova, Ekaterina & Gründler, Klaus & Hackenberger, Armin & Harter, Anina & Potrafke, Niklas & Schoors, Koen, 2023. "Crisis experience and the deep roots of COVID-19 vaccination preferences," European Economic Review, Elsevier, vol. 160(C).

    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:2308.10138. 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.