IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp13524.html
   My bibliography  Save this paper

Implementing the Panel Event Study

Author

Listed:
  • Clarke, Damian

    (University of Chile)

  • Schythe, Kathya Tapia

    (Universidad de Santiago de Chile)

Abstract

Many studies estimate the impact of exposure to some quasi-experimental policy or event using a panel event study design. These models, as a generalized extension of 'difference-in-differences' or two-way fixed effect models, allow for dynamic lags and leads to the event of interest to be estimated, while also controlling for fixed factors (often) by area and time. In this paper we discuss the set-up of the panel event study design in a range of situations, and lay out a number of practical considerations for its estimation. We describe a Stata command eventdd that allows for simple estimation, inference, and visualization of event study models in a range of circumstances. We then provide a number of examples to illustrate eventdd's use and flexibility, as well as its interaction with various native Stata routines, and other relevant user-written libraries such as reghdfe and boottest.

Suggested Citation

  • Clarke, Damian & Schythe, Kathya Tapia, 2020. "Implementing the Panel Event Study," IZA Discussion Papers 13524, Institute of Labor Economics (IZA).
  • Handle: RePEc:iza:izadps:dp13524
    as

    Download full text from publisher

    File URL: https://docs.iza.org/dp13524.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Simon Freyaldenhoven & Christian Hansen & Jesse M. Shapiro, 2019. "Pre-event Trends in the Panel Event-Study Design," American Economic Review, American Economic Association, vol. 109(9), pages 3307-3338, September.
    2. Suhonen, Tuomo & Karhunen, Hannu, 2019. "The intergenerational effects of parental higher education: Evidence from changes in university accessibility," Journal of Public Economics, Elsevier, vol. 176(C), pages 195-217.
    3. Kurt Schmidheiny & Sebastian Siegloch, 2019. "On Event Study Designs and Distributed-Lag Models: Equivalence, Generalization and Practical Implications," CESifo Working Paper Series 7481, CESifo.
    4. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2008. "Bootstrap-Based Improvements for Inference with Clustered Errors," The Review of Economics and Statistics, MIT Press, vol. 90(3), pages 414-427, August.
    5. Damian Clarke & Kathya Tapia Schythe, 2020. "EVENTDD: Stata module to panel event study models and generate event study plots," Statistical Software Components S458737, Boston College Department of Economics, revised 09 Dec 2023.
    6. David Roodman & James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2019. "Fast and wild: Bootstrap inference in Stata using boottest," Stata Journal, StataCorp LP, vol. 19(1), pages 4-60, March.
    7. 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.
    8. Betsey Stevenson & Justin Wolfers, 2006. "Bargaining in the Shadow of the Law: Divorce Laws and Family Distress," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 121(1), pages 267-288.
    9. Martha J. Bailey & Olga Malkova & Zoë M. McLaren, 2017. "Does Parents’ Access to Family Planning Increase Children’s Opportunities? Evidence from the War on Poverty and the Early Years of Title X," NBER Working Papers 23971, National Bureau of Economic Research, Inc.
    10. James G. MacKinnon & Matthew D. Webb, 2018. "The wild bootstrap for few (treated) clusters," Econometrics Journal, Royal Economic Society, vol. 21(2), pages 114-135, June.
    11. 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.
    12. Brantly Callaway & Pedro H. C. Sant'Anna, 2018. "Difference-in-Differences with Multiple Time Periods and an Application on the Minimum Wage and Employment," DETU Working Papers 1804, Department of Economics, Temple University.
    13. 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.
    14. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-In-Differences Estimates?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(1), pages 249-275.
    15. James G. MacKinnon & Matthew D. Webb, 2019. "Wild Bootstrap Randomization Inference for Few Treated Clusters," Advances in Econometrics, in: The Econometrics of Complex Survey Data, volume 39, pages 61-85, Emerald Group Publishing Limited.
    16. James G. MacKinnon & Matthew D. Webb, 2017. "Wild Bootstrap Inference for Wildly Different Cluster Sizes," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(2), pages 233-254, March.
    17. Martha J. Bailey & Olga Malkova & Zoe M. McLaren, 2017. "Does Parents� Access To Family Planning Increase Children�S Opportunities? Evidence From The War On Poverty And The Early Years Of Title X," Working Papers 17-67, Center for Economic Studies, U.S. Census Bureau.
    18. Athey, Susan & Imbens, Guido W., 2022. "Design-based analysis in Difference-In-Differences settings with staggered adoption," Journal of Econometrics, Elsevier, vol. 226(1), pages 62-79.
    19. Timothy G. Conley & Christopher R. Taber, 2011. "Inference with "Difference in Differences" with a Small Number of Policy Changes," The Review of Economics and Statistics, MIT Press, vol. 93(1), pages 113-125, February.
    20. Andrew Goodman-Bacon, 2018. "Difference-in-Differences with Variation in Treatment Timing," NBER Working Papers 25018, National Bureau of Economic Research, Inc.
    21. James G. MacKinnon & Matthew D. Webb, 2018. "The wild bootstrap for few (treated) clusters," Econometrics Journal, Royal Economic Society, vol. 21(2), pages 114-135, June.
    22. Ariella Kahn-Lang & Kevin Lang, 2020. "The Promise and Pitfalls of Differences-in-Differences: Reflections on 16 and Pregnant and Other Applications," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 38(3), pages 613-620, July.
    23. Dimitrovová, Klára & Perelman, Julian & Serrano-Alarcón, Manuel, 2020. "Effect of a national primary care reform on avoidable hospital admissions (2000–2015): A difference-in-difference analysis," Social Science & Medicine, Elsevier, vol. 252(C).
    24. 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.
    25. Fausto Pacicco & Luigi Vena & Andrea Venegoni, 2018. "Event study estimations using Stata: The estudy command," Stata Journal, StataCorp LP, vol. 18(2), pages 461-476, June.
    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. Bruno Ferman, 2023. "Inference in difference‐in‐differences: How much should we trust in independent clusters?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(3), pages 358-369, April.
    3. MacKinnon, James G. & Nielsen, Morten Ørregaard & Webb, Matthew D., 2023. "Cluster-robust inference: A guide to empirical practice," Journal of Econometrics, Elsevier, vol. 232(2), pages 272-299.
    4. Hansen, Bruce E. & Lee, Seojeong, 2019. "Asymptotic theory for clustered samples," Journal of Econometrics, Elsevier, vol. 210(2), pages 268-290.
    5. James G. MacKinnon, 2019. "How cluster-robust inference is changing applied econometrics," Canadian Journal of Economics, Canadian Economics Association, vol. 52(3), pages 851-881, August.
    6. Dorner, Matthias & Görlitz, Katja, 2020. "Training, wages and a missing school graduation cohort," IAB-Discussion Paper 202028, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    7. MacKinnon, James G. & Nielsen, Morten Ørregaard & Webb, Matthew D., 2023. "Testing for the appropriate level of clustering in linear regression models," Journal of Econometrics, Elsevier, vol. 235(2), pages 2027-2056.
    8. Huber, Martin, 2019. "An introduction to flexible methods for policy evaluation," FSES Working Papers 504, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
    9. Matthew D. Webb, 2023. "Reworking wild bootstrap‐based inference for clustered errors," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 56(3), pages 839-858, August.
    10. Kayaoglu, Aysegul, 2022. "Do refugees cause crime?," World Development, Elsevier, vol. 154(C).
    11. García-Ramos, Aixa, 2021. "Divorce laws and intimate partner violence: Evidence from Mexico," Journal of Development Economics, Elsevier, vol. 150(C).
    12. Andrea Ciaccio, 2023. "The Impact of a Cost-containment Measure on the Quality of Regional Health Services in Italy: a Parametric and a Non-parametric Approach," Working Papers 2023: 24, Department of Economics, University of Venice "Ca' Foscari".
    13. James G. MacKinnon & Morten Ørregaard Nielsen & Matthew D. Webb, 2021. "Wild Bootstrap and Asymptotic Inference With Multiway Clustering," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(2), pages 505-519, March.
    14. James G. MacKinnon & Morten {O}rregaard Nielsen & Matthew D. Webb, 2024. "Cluster-robust jackknife and bootstrap inference for binary response models," Papers 2406.00650, arXiv.org.
    15. 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.
    16. Andreas Olden & Jarle Møen, 2022. "The triple difference estimator [Semiparametric difference-in-differences estimators]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 531-553.
    17. Goussé, Marion & Leturcq, Marion, 2022. "More or less unmarried. The impact of legal settings of cohabitation on labour market outcomes," European Economic Review, Elsevier, vol. 149(C).
    18. MacKinnon, James G. & Webb, Matthew D., 2020. "Randomization inference for difference-in-differences with few treated clusters," Journal of Econometrics, Elsevier, vol. 218(2), pages 435-450.
    19. James G. MacKinnon & Matthew D. Webb, 2020. "When and How to Deal with Clustered Errors in Regression Models," Working Paper 1421, Economics Department, Queen's University.
    20. Luis Alvarez & Bruno Ferman, 2020. "Inference in Difference-in-Differences with Few Treated Units and Spatial Correlation," Papers 2006.16997, arXiv.org, revised Apr 2023.

    More about this item

    Keywords

    inference; event studies; difference-in-differences; estimation; visualization;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software

    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:iza:izadps:dp13524. 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: Holger Hinte (email available below). General contact details of provider: https://edirc.repec.org/data/izaaade.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.