Implementing the Panel Event Study

Clarke, Damian and Tapia Schythe, Kathya (2020): Implementing the Panel Event Study.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: event studies, difference-in-differences, estimation, inference, visualization
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C54 - Quantitative Policy Modeling; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C63 - Computational Techniques ; Simulation Modeling; C - Mathematical and Quantitative Methods > C8 - Data Collection and Data Estimation Methodology ; Computer Programs > C87 - Econometric Software

References

Abraham, S., and L. Sun. 2018. Estimating Dynamic Treatment Effects in Event Studies with Heterogeneous Treatment Effects. Angrist, J. D., and J.-S. Pischke. 2008. Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press. Athey, S., and G. W. Imbens. 2018. Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption. Working Paper 24963, National Bureau of Economic Research. Bailey, M. J., O. Malkova, and Z. M. McLaren. 2018. Does Access to Family Planning Increase Children’s Opportunities? Evidence from the War on Poverty and the Early Years of Title X. Journal of Human Resources. http://jhr.uwpress.org/content/early/2018/07/03/jhr.55.1.1216-8401R1.abstract. Bertrand, M., E. Duflo, and S. Mullainathan. 2004. How Much Should We Trust Differences-In-Differences Estimates? The Quarterly Journal of Economics 119(1): 249–275. Borusyak, K., and X. Jaravel. 2018. Revisiting Event Study Designs, with an Application to the Estimation of the Marginal Propensity to Consume. mimeo. https://scholar.harvard.edu/files/borusyak/files/event_studies_may8_website.pdf. Callaway, B., and P. 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. https://ideas.repec.org/p/tem/wpaper/1804.html. Cameron, A., and D. Miller. 2015. A Practitioner’s Guide to Cluster-Robust Inference. Journal of Human Resources 50(2): 317–372. Cameron, A. C., J. B. Gelbach, and D. L. Miller. 2008. Bootstrap-based improvements for inference with clustered errors. Review of Economics and Statistics 90: 414–427. de Chaisemartin, C., and X. D’Haultfoeuille. 2019. Two-way Fixed Effects Estimators with Heterogeneous Treatment Effects. NBER Working Papers 25904, National Bureau of Economic Research, Inc. https://ideas.repec.org/p/nbr/nberwo/25904.html. Clarke, D., and K. Tapia Schythe. 2020. EVENTDD: Stata module to panel event study models and generate event study plots. https://EconPapers.repec.org/RePEc:boc:bocode:s458737. Conley, T. G., and C. R. Taber. 2011. Inference with “Difference in Differences” with a Small Number of Policy Changes. The Review of Economics and Statistics 93(1): 113–125. Correia, S. 2016. Linear Models with High-Dimensional Fixed Effects: An Efficient and Feasible Estimator. Technical report. Working Paper. http://scorreia.com/research/hdfe.pdf. Dimitrovová, K., J. Perelman, and M. Serrano-Alarcón. 2020. Effect of a national primary care reform on avoidable hospital admissions (2000–2015): A difference-in-difference analysis. Social Science & Medicine 252: 112908. http://www.sciencedirect.com/science/article/pii/S0277953620301271. Freyaldenhoven, S., C. Hansen, and J. M. Shapiro. 2019. Pre-event Trends in the Panel Event-Study Design. American Economic Review 109(9): 3307–38. http://www.aeaweb.org/articles?id=10.1257/aer.20180609. Goodman-Bacon, A. 2018. Difference-in-Differences with Variation in Treatment Timing. Working Paper 25018, National Bureau of Economic Research. http://www.nber.org/papers/w25018. Goodman-Bacon, A., T. Goldring, and A. Nichols. 2019. BACONDECOMP: Stata module to perform a Bacon decomposition of difference-in-differences estimation. Statistical Software Components, Boston College Department of Economics. https://ideas.repec.org/c/boc/bocode/s458676.html. Kahn-Lang, A., and K. Lang. 2019. The Promise and Pitfalls of Differences-in-Differences: Reflections on 16 and Pregnant and Other Applications. Journal of Business & Economic Statistics 0(0): 1–14. Mackinnon, J., and M. Webb. 2017. Wild Bootstrap Inference for Wildly Different Cluster Sizes. Journal of Applied Econometrics 32: 233–254. Mackinnon, J., and M. Webb. 2018. The wild bootstrap for few (treated) clusters. The Econometrics Journal 21: 114–135. Pacicco, F., L. Vena, and A. Venegoni. 2018. Event Study Estimations Using Stata: The Estudy Command. The Stata Journal 18(2): 461–476. Rambachan, A., and J. Roth. 2019. An Honest Approach to Parallel Trends. Roodman, D. 2015. BOOTTEST: Stata module to provide fast execution of the wild bootstrap with null imposed. Statistical Software Components, Boston College Department of Economics. https://ideas.repec.org/c/boc/bocode/s458121.html. Roodman, D., M. Ø. Nielsen, J. G. MacKinnon, and M. D. Webb. 2019. Fast and wild: Bootstrap inference in Stata using boottest. The Stata Journal 19(1): 4–60. Roth, J. 2019. Pre-test with Caution: Event-study Estimates After Testing for Parallel Trends. Schmidheiny, K., and S. Siegloch. 2019. On Event Study Designs and Distributed-Lag Models: Equivalence, Generalization and Practical Implications. IZA Discussion Papers 12079, Institute of Labor Economics (IZA). Stevenson, B., and J. Wolfers. 2006. Bargaining in the Shadow of the Law: Divorce Laws and Family Distress*. The Quarterly Journal of Economics 121(1): 267–288. Suhonen, T., and H. Karhunen. 2019. The intergenerational effects of parental higher education: Evidence from changes in university accessibility. Journal of Public Economics 176:195 – 217. http://www.sciencedirect.com/science/article/pii/S0047272719300945. Venkataramani, A. S., E. F. Bair, R. L. O’Brien, and A. C. Tsai. 2020. Association Between Automotive Assembly Plant Closures and Opioid Overdose Mortality in the United States: A Difference-in-Differences Analysis. JAMA Internal Medicine 180(2): 254–262. https://doi.org/10.1001/jamainternmed.2019.5686.