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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

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    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
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    References listed on IDEAS

    as
    1. Schmidheiny, Kurt & Siegloch, Sebastian, 2019. "On Event Study Designs and Distributed-Lag Models: Equivalence, Generalization and Practical Implications," IZA Discussion Papers 12079, IZA Network @ LISER.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Kurt Schmidheiny & Sebastian Siegloch, 2023. "On event studies and distributed‐lags in two‐way fixed effects models: Identification, equivalence, and generalization," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(5), pages 695-713, August.
    7. Alberto Abadie, 2005. "Semiparametric Difference-in-Differences Estimators," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(1), pages 1-19.
    8. Barkowski, Scott, 2021. "Interpretation of nonlinear difference-in-differences: the role of the parallel trends assumption," MPRA Paper 108975, University Library of Munich, Germany.
    9. Manasi Deshpande & Yue Li, 2019. "Who Is Screened Out? Application Costs and the Targeting of Disability Programs," American Economic Journal: Economic Policy, American Economic Association, vol. 11(4), pages 213-248, November.
    10. 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.
    11. Ai, Chunrong & Norton, Edward C., 2003. "Interaction terms in logit and probit models," Economics Letters, Elsevier, vol. 80(1), pages 123-129, July.
    12. 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.
    13. Goodman-Bacon, Andrew, 2021. "Difference-in-differences with variation in treatment timing," Journal of Econometrics, Elsevier, vol. 225(2), pages 254-277.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. John Gardner, 2022. "Two-stage differences in differences," Papers 2207.05943, arXiv.org.
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    Keywords

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    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

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