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Modern DiD, Event-study Design, and other Topics in Applied EconometricsTopics in Political Economy and Social Mobility (ECO-AD-MODERNDID)

ECO-AD-MODERNDID

Purpose

 

Description

This is a 20-hour topics course covering recent advances in DiD and Event-study Design. The TA for the course TBA (TBA@eui.eu). There will be four TA sessions, Day/time TBA. The main purpose of this course is to introduce researchers to (1) the key problems that may arise when using ‘standard’ DiD/Event Study Design in applied work, and (2) possible solutions to address such problems; (3) selected topics of importance in empirical work, mostly in what we call  “applied micro”.

Evaluation:
There will be 2 problem sets and either a take home exam or a class presentation to prepare (we will decide based on how many of you will take the course for credit). On “modern DiD / event-study design” there are already some excellent surveys that review much of the recent advances (actually so many that it’s becoming difficult to keep track even of the surveys...):

¿ de Chaisemartin, C. and X. D’Haultfoeuille (2023). Two-way fixed effects and differences-in-differences with heterogeneous treatment effects: A survey. Econometrics Journal, 26(3), C1-C30.
¿ Roth, J., P. H. Sant’Anna, A. Bilinski, and J. Poe (2023). What’s trending in Difference-in- Differences? A synthesis of the recent econometrics literature. Journal of Econometrics, 235(2), 2218-2244.
¿ Arkhangelsky and Imbens (2024) Causal models for longitudinal and panel data: A survey. The Econometrics Journal, 27(3), C1–C61.
¿ Baker, A., B. Callaway, S. Cunningham, A. Goodman-Bacon, P. Sant’Anna (2025). Difference-in- Differences Designs: A Practitioner’s Guide. Journal of Economic Literature (Forthcoming).
¿ de Chaisemartin, C. and X. D’Haultfoeuille (2025). Credible Answers to Hard Questions: Differencesin- Differences for Natural Experiments.

There is also by now a whole lot of video material online on ‘modern DiD’, and here I only list/link to a few things. Silvia Vannutelli has a nice video introduction to this literature (video is from 2020, much has already changed since then!). For a longer and more recent survey see Jonathan Roth. For Wooldridge’s view see here. For a nice discussion on identification assumptions in DD in general see Kahn-Lang and Lang (2020): you should read this paper!

Other Selected References
This is a rapidly growing literature. We will start from the general framework, cover some of these papers in some detail, and only mention / skim through some others.
¿ Arkhangelsky, D., S. Athey, D. A. Hirshberg, G. W. Imbens, and S. Wager (2021). Synthetic difference-in-differences. American Economic Review 111(12), 4088-4118.
¿ Borusyak, Jaravel, and Spiess (2024). Revisiting Event-Study Designs: Robust and Efficient Estimation. Review of Economic Studies 91(6), 3253–3285.
¿ Callaway, B. and P. Sant’Anna (2021). Difference-in-differences with multiple time periods. Journal of Econometrics 225(2), 200-230.
¿ Callaway, B., A. Goodman-Bacon and P. Sant’Anna (2024). Difference-in-Differences with a Continuous Treatment. Working Paper. Also see AER P&P 2024 for a shorter, earlier version.
¿ de Chaisemartin, C. and X. D’Haultfoeuille (2020). Two-way fixed effects estimators with heterogeneous treatment effects. American Economic Review 110(9), 2964-2996.
¿ de Chaisemartin, C. and X. D’Haultfoeuille (2024). Difference-in-Differences estimators of intertemporal treatment effects. Forthcoming Review of Economics and Statistics.
¿ de Chaisemartin, C., X. D’Haultfoeuille, F. Pasquier, D. Sow, AND G. Vazquez-Bare (2025). Differencein- Differences Estimators for Continuous Treatments and Instruments with Stayers. Working Paper.
¿ Deb, P., E. Norton, J. Wooldridge, and J. Zabel (2024) A Flexible, Heterogeneous Treatment Effects Difference-In-Differences Estimator For Repeated Cross-Sections. NBER Working Paper 33026.
¿ Dube, A., D. Girardi, O. Jord`a, and A. Taylor (2025). A Local Projections Approach to Differencein- Differences Event Studies. Journal of Applied Econometrics (Forthcoming).
¿ Ghanem, D., P. Sant’Anna, and K. W¨uthrich (2025). Selection and parallel trends. Working Paper.
¿ Goodman-Bacon, A. (2021). Difference-in-differences with variation in treatment timing. Journal of Econometrics 225(2), 254-277.
¿ Kahn-Lang, and Lang (2020). The promise and pitfalls of differences-in-differences: Reflections on 16 and pregnant and other applications. Journal of Business & Economic Statistics 38(3), 613-620.
¿ Rambachan, A. and J. Roth (2023). A more credible approach to parallel trends. Review of Economic Studies, 90, 2555-2591.
¿ Roth, J. (2022). Pretest with caution: Event-study estimates after testing for parallel trends. American Economic Review: Insights 4(3), 305-22.
¿ Sun, L. and S. Abraham (2021). Estimating dynamic treatment effects in event studies with heterogeneous treatment effects. Journal of Econometrics 225(2), 175-199.
¿ Wooldridge, J. M. (2025). Two-way fixed effects, the two-way Mundlak regression, and differencein- differences estimators. Empirical Economics.

Other topics that we will likely cover (also depending on time!) are:
1. Non-standard standard errors and inference: Adjusting standard errors with non-i.i.d. data in cross-sectional and panel data &/or with small samples (Bertrand et al. 2004, Cameron et al.
2011, Cameron and Miller 2015, MacKinnon et al. 2023, Fafchamps and Gubert 2007, Colella et al.
2019, Kelly 2019, Voth 2021, Athey and Imbens 2017, Cameron and Miller 2022 slides on recent developments in cluster-robust inference); the bootstrap (Horowitz 2001, Horowitz 2019, Imbens
2021); randomization inference (or not) (Young 2019, Simonsohn 2021, Heß 2017);
2. Inference with multiple hypothesis and family-wise error rates (Holm 1979, Benjamini and
Hochberg 1995, Clarke et al. 2020).
3. Survey Design and Sampling Weights: Deaton (1997, Ch. 1, 2.1, 2.2), Solon et al. (2015).
4. (If time allows) Quantile Regression: Buchinsky (1998), Deaton (1997, pp. 80-85). Some good general references are Deaton (1997) (available here), Wooldridge (2002), Angrist and Pischke (2009), Athey and Imbens (2017), Cameron and Trivedi (2005) (related material available here).
References
Angrist, J. D. and J.-S. Pischke (2009). Mostly Harmless Econometrics: An Empiricist’s companion. Princeton, NJ: Princeton University Press.
Athey, S. and G. Imbens (2017). The econometrics of randomized experiments. In A. V. Banerjee and E. Duflo (Eds.), Handbook of Field Experiments, Volume 1 of Handbook of Economic Field Experiments, Chapter 3, pp. 73–140. North-Holland.

Benjamini, Y. and Y. Hochberg (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological) 57 (1), 289–300.
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.
Buchinsky, M. (1998). Recent advanced in quantile regression models: a practical guideline for empirical research.
Journal of Human Resources 33 (1),88–126.
Cameron, A. C., J. B. Gelbach, and D. L. Miller (2011). Robust inference with multi-way clustering. Journal of
Business and Economic Statistics 29 (2), 238–249.
Cameron, A. C. and D. L. Miller (2015). A practitioner’s guide to cluster-robust inference. Journal of human
resources 50 (2), 317–372.
Cameron, A. C. and P. K. Trivedi (2005). Microeconometrics: methods and applications. New York: Cambridge University Press.
Clarke, D., J. P. Romano, and M. Wolf (2020). The Romano–Wolf multiple-hypothesis correction in Stata. The
Stata Journal 20 (4), 812–843.
Colella, F., R. Lalive, S. O. Sakalli, and M. Thoenig (2019). Inference with arbitrary clustering. IZA Discussion
Paper No. 12584.
Deaton, A. (1997). The Analysis of Household Surveys: A Microeconometric Approach to Development Policy. The
Johns Hopkins University Press (for the World Bank). Pdf available Here.
Fafchamps, M. and F. Gubert (2007). The formation of risk sharing networks. Journal of Development Economics
83 (2), 326 �� 350. Papers from a Symposium: The Social Dimensions of Microeconomic Behaviour in
Low-Income Communities.
Heß, S. (2017). Randomization inference with Stata: A guide and software. The Stata Journal 17 (3), 630–651.
Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian journal of statistics 6 (2),
65–70.
Horowitz, J. (2001). The Bootstrap. In Handbook of Econometrics, Volume V. North-Holland.
Horowitz, J. L. (2019). Bootstrap methods in econometrics. Annual Review of Economics 11, 193–224.
Imbens, G. W. (2021). Statistical significance, p-values, and the reporting of uncertainty. Journal of Economic
Perspectives 35 (3), 157–74.
Kelly, M. (2019). The standard errors of persistence. CEPR Discussion paper no. DP13783.
MacKinnon, J. G., M. Ø. Nielsen, and M. D. Webb (2023). Cluster-robust inference: A guide to empirical practice.
Journal of Econometrics 232 (2), 272–299.
Simonsohn, U. (2021). Just run robuster standard errors: A commentary on Young. Working Paper, available at
http://urisohn.com/43.
Solon, G., S. J. Haider, and J. M. Wooldridge (2015). What are we weighting for? The Journal of Human
Resources 50 (2), 301–316.
Voth, H.-J. (2021). Persistence - Myth and mystery. In A. Bisin and G. Federico (Eds.), The Handbook of Historical
Economics, Chapter 9, pp. 243–267. Academic Press.
Wooldridge, J. (2002). Econometrics of cross section and panel data. Cambridge, MA: MIT Press.
Young, A. (2019). Channeling fisher: Randomization tests and the statistical insignificance of seemingly significant
experimental results. The Quarterly Journal of Economics 134 (2), 557–598.

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