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Modeling Event Studies with Heterogeneous Treatment Effects

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Abstract

This paper develops a simple approach to overcome the shortcomings of using a standard, single treatment–effect event study to assess the ability of an empirical model to measure heterogeneous treatment effects. Equally as important, we discuss how the standard errors reported in a typical event-study analysis for the posttreatment event-time effects are, without additional information, of limited use for assessing posttreatment variations in the treatment effects. The simple reformulation of the standard event—study approach described and illustrated with artificially constructed data in this paper overcomes the limitations of conventional event-study analyses.

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  • Laura Argys & Thomas Mroz & M. Melinda Pitts, 2023. "Modeling Event Studies with Heterogeneous Treatment Effects," FRB Atlanta Working Paper 2023-11, Federal Reserve Bank of Atlanta.
    • Handle: RePEc:fip:fedawp:96718
    DOI: 10.29338/wp2023-11
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    References listed on IDEAS

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    1. Douglas L. Miller, 2023. "An Introductory Guide to Event Study Models," Journal of Economic Perspectives, American Economic Association, vol. 37(2), pages 203-230, Spring.
    2. 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.
    3. Janet Currie & Henrik Kleven & Esmée Zwiers, 2020. "Technology and Big Data Are Changing Economics: Mining Text to Track Methods," AEA Papers and Proceedings, American Economic Association, vol. 110, pages 42-48, May.
    4. Goodman-Bacon, Andrew, 2021. "Difference-in-differences with variation in treatment timing," Journal of Econometrics, Elsevier, vol. 225(2), pages 254-277.
    5. 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.
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    Keywords

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    JEL classification:

    • 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

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