IDEAS home Printed from https://ideas.repec.org/p/cbi/wpaper/4-rt-24.html
   My bibliography  Save this paper

Estimating effects of staggered intervention with count and binary outcomes: a simulation study

Author

Listed:
  • Yadav, Anil

    (Central Bank of Ireland)

  • McHale, John

    (University of Galway)

  • Harold, Jason

    (University of Galway)

  • O'Neill, Stephen

    (London School of Hygiene & Tropical Medicine)

Abstract

Difference-in-Differences and Event-study methods with staggered intervention may provide biased estimates when these approaches are implemented using a two-way fixed effect (TWFE) estimator in the presence of heterogeneous effects. Recent literature proposed alternative estimators that are unbiased, however to date, attention has primarily focused on linear outcome models. This study addresses this gap by extending five of these alternative estimators to count and binary outcomes and assessing their accuracy against the TWFE estimator in Monte Carlo simulations. While unbiased for linear models, some of the estimators yield biased estimates for nonlinear outcomes. An application revisits the statistical association between citations and star coauthorship.

Suggested Citation

  • Yadav, Anil & McHale, John & Harold, Jason & O'Neill, Stephen, 2024. "Estimating effects of staggered intervention with count and binary outcomes: a simulation study," Research Technical Papers 4/RT/24, Central Bank of Ireland.
    • Handle: RePEc:cbi:wpaper:4/rt/24
    as

    Download full text from publisher

    File URL: https://www.centralbank.ie/docs/default-source/publications/research-technical-papers/estimating-effects-of-staggered-intervention-with-count-and-binary-outcomes.pdf?sfvrsn=f937611a_7
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fernández-Val, Iván & Weidner, Martin, 2016. "Individual and time effects in nonlinear panel models with large N, T," Journal of Econometrics, Elsevier, vol. 192(1), pages 291-312.
    2. Gourieroux, Christian & Monfort, Alain & Trognon, Alain, 1984. "Pseudo Maximum Likelihood Methods: Applications to Poisson Models," Econometrica, Econometric Society, vol. 52(3), pages 701-720, May.
    3. Clément de Chaisemartin & Xavier D'Haultfœuille, 2020. "Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects," American Economic Review, American Economic Association, vol. 110(9), pages 2964-2996, September.
    4. Clément de Chaisemartin & Xavier D’Haultfœuille, 2023. "Two-way fixed effects and differences-in-differences with heterogeneous treatment effects: a survey," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 1-30.
    5. Alberto Abadie, 2005. "Semiparametric Difference-in-Differences Estimators," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(1), pages 1-19.
    6. Barkowski, Scott, 2021. "Interpretation of nonlinear difference-in-differences: the role of the parallel trends assumption," MPRA Paper 108975, University Library of Munich, Germany.
    7. Susan Athey & Guido W. Imbens, 2006. "Identification and Inference in Nonlinear Difference-in-Differences Models," Econometrica, Econometric Society, vol. 74(2), pages 431-497, March.
    8. Sun, Liyang & Abraham, Sarah, 2021. "Estimating dynamic treatment effects in event studies with heterogeneous treatment effects," Journal of Econometrics, Elsevier, vol. 225(2), pages 175-199.
    9. Ai, Chunrong & Norton, Edward C., 2003. "Interaction terms in logit and probit models," Economics Letters, Elsevier, vol. 80(1), pages 123-129, July.
    10. Baker, Andrew C. & Larcker, David F. & Wang, Charles C.Y., 2022. "How much should we trust staggered difference-in-differences estimates?," Journal of Financial Economics, Elsevier, vol. 144(2), pages 370-395.
    11. Goodman-Bacon, Andrew, 2021. "Difference-in-differences with variation in treatment timing," Journal of Econometrics, Elsevier, vol. 225(2), pages 254-277.
    12. Callaway, Brantly & Sant’Anna, Pedro H.C., 2021. "Difference-in-Differences with multiple time periods," Journal of Econometrics, Elsevier, vol. 225(2), pages 200-230.
    13. Doruk Cengiz & Arindrajit Dube & Attila Lindner & Ben Zipperer, 2019. "The Effect of Minimum Wages on Low-Wage Jobs," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(3), pages 1405-1454.
    14. Jeffrey M Wooldridge, 2023. "Simple approaches to nonlinear difference-in-differences with panel data," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 31-66.
    15. John Gardner, 2022. "Two-stage differences in differences," Papers 2207.05943, arXiv.org.
    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. Roth, Jonathan & Sant’Anna, Pedro H.C. & Bilinski, Alyssa & Poe, John, 2023. "What’s trending in difference-in-differences? A synthesis of the recent econometrics literature," Journal of Econometrics, Elsevier, vol. 235(2), pages 2218-2244.
    2. Jeffrey M Wooldridge, 2023. "Simple approaches to nonlinear difference-in-differences with panel data," The Econometrics Journal, Royal Economic Society, vol. 26(3), pages 31-66.
    3. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    4. Chen, Jidong & Shi, Xinzheng & Zhang, Ming-ang & Zhang, Sihan, 2024. "Centralization of environmental administration and air pollution: Evidence from China," Journal of Environmental Economics and Management, Elsevier, vol. 126(C).
    5. Melnik, Walter & Smyth, Andrew, 2024. "R&D tax credits and innovation," Journal of Public Economics, Elsevier, vol. 236(C).
    6. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    7. Lennon, Conor & Maclean, Johanna Catherine & Teltser, Keith, 2025. "Ridesharing and substance use disorder treatment," Journal of Health Economics, Elsevier, vol. 99(C).
    8. Callaway, Brantly & Li, Tong, 2023. "Policy evaluation during a pandemic," Journal of Econometrics, Elsevier, vol. 236(1).
    9. 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.
    10. Rik Chakraborti & Gavin Roberts, 2023. "How price-gouging regulation undermined COVID-19 mitigation: county-level evidence of unintended consequences," Public Choice, Springer, vol. 196(1), pages 51-83, July.
    11. Cl'ement de Chaisemartin & Xavier D'Haultf{oe}uille, 2021. "Two-Way Fixed Effects and Differences-in-Differences with Heterogeneous Treatment Effects: A Survey," Papers 2112.04565, arXiv.org, revised Jun 2022.
    12. Davidson, Carl & Heyman, Fredrik & Matusz, Steven & Sjöholm, Fredrik & Chun Zhu, Susan, 2022. "How International Experience Helps Shape Labor Market Outcomes," Working Paper Series 1453, Research Institute of Industrial Economics, revised 09 Jun 2024.
    13. Balia, S.; & Brau, R.; & Pau, S.;, 2025. "One plus one makes less than two? Consolidation policies and mortality in the Italian NHS," Health, Econometrics and Data Group (HEDG) Working Papers 25/02, HEDG, c/o Department of Economics, University of York.
    14. Nieto, Adrián, 2022. "Can subsidies to permanent employment change fertility decisions?," Labour Economics, Elsevier, vol. 78(C).
    15. Brick, Kerri & De Martino, Samantha & Visser, Martine, 2023. "Behavioural nudges for water conservation in unequal settings: Experimental evidence from Cape Town," Journal of Environmental Economics and Management, Elsevier, vol. 121(C).
    16. Drolsbach, Chiara Patricia & Gail, Maximilian Maurice & Klotz, Phil-Adrian, 2023. "Pass-through of temporary fuel tax reductions: Evidence from Europe," Energy Policy, Elsevier, vol. 183(C).
    17. Caliendo, Marco & Olthaus, Rebecca & Pestel, Nico, 2025. "Long-term employment effects of the minimum wage in Germany: New data and estimators," Labour Economics, Elsevier, vol. 92(C).
    18. Andrea Mattozzi & Samuel Nocito & Francesco Sobbrio, 2022. "Fact-Checking Politicians," CESifo Working Paper Series 10122, CESifo.
    19. Elisa Facchetti & Lorenzo Neri & Marco Ovidi, 2021. "Should you Meet The Parents? The impact of information on non-test score attributes on school choice," DISCE - Working Papers del Dipartimento di Economia e Finanza def113, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE).
    20. Yang Ding & Fernando Moreira, 2025. "Funding and productivity: Does winning grants affect the scientific productivity of recipients? Evidence from the social sciences and economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(3), pages 1831-1870, March.

    More about this item

    Keywords

    Nonlinear difference-in-differences and Event-study; Staggered intervention; Count and Binary outcomes; Treatment effect heterogeneity.;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

    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:cbi:wpaper:4/rt/24. 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: Fiona Farrelly (email available below). General contact details of provider: https://edirc.repec.org/data/cbigvie.html .

    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.