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Cited by 427Advanced Causal Inference TechniquesSpatial and Panel Data AnalysisEvaluation and Performance Assessment

Pre-Event Trends in the Panel Event-Study Design

Open access
Freyaldenhoven, Simon, Hansen, Christian, Shapiro, Jesse M.·2019·American Economic Review
DOI 10.1257/aer.20180609
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Abstract

We consider a linear panel event-study design in which unobserved confounds may be related both to the outcome and to the policy variable of interest. We provide sufficient conditions to identify the causal effect of the policy by exploiting covariates related to the policy only through the confounds. Our model implies a set of moment equations that are linear in parameters. The effect of the policy can be estimated by 2SLS, and causal inference is valid even when endogeneity leads to pre-event trends (“pre-trends”) in the outcome. Alternative approaches perform poorly in our simulations. (JEL C23, C26)

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  • Traditional pre-trend tests can mislead because they may lack enough statistical power to detect a parallel trends violation even when non-parallel pre-intervention trends exist.

    “However, as several papers have noted [20, 24, 76-77], these tests can be misleading because they may not have adequate statistical power to detect a violation, even if there are non-parallel pre-intervention trend differences between treatment and comparison groups [20, 24, 76-77].”

    From Difference-in-Differences for Health Policy and Practice: A Review of Modern Methods (2025) · cites this paper as [77]

  • Even if there are pre-existing differences in trends, the tests described above may fail to reject owing to low power.

    “A second issue is that even if there are pre-existing differences in trends, the tests described above may fail to reject owing to low power (Bilinski and Hatfield, 2018; Freyaldenhoven, Hansen a 2019; Kahn-Lang and Lang, 2020; Roth, 2022).”

    From What's trending in difference-in-differences? A synthesis of the recent econometrics literature (2023) · cites this paper as Freyaldenhoven, Hansen a 2019

  • Freyaldenhoven et al. (2019) propose a GMM-based estimation strategy that allows for parallel trends to be violated when there exists a covariate assumed to be affected by the same confounds as the outcome but not by the treatment itself.

    “Finally, Freyaldenhoven et al. (2019) propose a GMM-based estimation strategy that allows for parallel trends to be violated when there exists a covariate assumed to be affected by the same confounds as the outcome but not by the treatment itself.”

    From What's trending in difference-in-differences? A synthesis of the recent econometrics literature (2023) · cites this paper as Freyaldenhoven et al. (2019)

  • Recent research has shown that pre-trends tests may suffer from low power.

    “recent research has shown that they may suffer from low power (Freyaldenhoven et al., 2019;Bilinski and Hatfield, 2020;Kahn-Lang and Lang, 2020;Roth, 2022)”

    From A More Credible Approach to Parallel Trends (2023) · cites this paper as Freyaldenhoven et al., 2019

  • Freyaldenhoven et al. (2019) propose a method that allows for violations of the parallel trends assumption but requires an additional covariate that is affected by the same confounding factors as the outcome but not by the treatment of interest.

    “Freyaldenhoven et al. (2019) propose a method that allows for violations of the parallel trends assumption but requires an additional covariate that is affected by the same confounding factors as the outcome but not by the treatment of interest.”

    From A More Credible Approach to Parallel Trends (2023) · cites this paper as Freyaldenhoven et al. (2019)

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Research works printed but not linked: 13

  1. [2]2. Abbring, J. H. and J. J. Heckman (2007). Econometric evaluation of social programs, part III: Distributional treatment effects, dynamic treatment effects, dynamic discrete choice, and gen- eral equilibrium policy evaluation. In J. J. Heckman and E. E. Leamer (Eds.), Handbook of Econometrics, Volume 6, Chapter 72, pp. 5145-5303. Elsevier.
  2. [4]4. Aigner, D. J., C. Hsiao, A. Kapteyn, and T. Wansbeek (1984). Latent variable models in econo- metrics. In Z. Griliches and M. D. Intriligator (Eds.), Handbook of Econometrics, Volume 2, Chapter 23, pp. 1321-1393. Elsevier.
  3. [20]20. Brown, C. (1999). Minimum wages, employment, and the distribution of income. In O. C. Ashen- felter and D. Card (Eds.), Handbook of Labor Economics, Volume 3, Chapter 32, pp. 2101 - 2163. Elsevier.
  4. [22]22. Card, D. E. and A. B. Krueger (1995). Myth and Measurement: The New Economics of the Minimum Wage. Princeton University Press.
  5. [27]27. Dixit, A. K. and R. S. Pindyck (1994). Investment under uncertainty. Princeton University Press.
  6. [38]38. Heckman, J. J. and R. Robb (1985, 10). Alternative methods for evaluating the impact of interven- tions. In J. J. Heckman and B. S. Singer (Eds.), Longitudinal Analysis of Labor Market Data:, pp. 156-246. Cambridge: Cambridge University Press.
  7. [39]39. Heckman, J. J. and E. J. Vytlacil (2007). Econometric evaluation of social programs, part II: Using the marginal treatment effect to organize alternative econometric estimators to evaluate social programs, and to forecast their effects in new environments. In J. J. Heckman and E. E. Leamer (Eds.), Handbook of Econometrics, Volume 6, Chapter 71, pp. 4875-5143. Elsevier.
  8. [43]43. Kahn-Lang, A. and K. Lang (2018). The promise and pitfalls of differences-in-differences: Reflec- tions on '16 and Pregnant' and other applications. Journal of Business & Economic Statistics. Forthcoming.
  9. [46]46. MacKinlay, A. C. (1997). Event studies in economics and finance. Journal of Economic Litera- ture 35(1), 13-39.
  10. [47]47. Matzkin, R. L. (2007). Nonparametric identification. In J. J. Heckman and E. E. Leamer (Eds.), Handbook of Econometrics, Volume 6, Chapter 73, pp. 5307 -5368. Elsevier.
  11. [50]50. Neumark, D. and W. L. Wascher (2007). Minimum wages and employment. Foundations and Trends R in Microeconomics 3(1-2), 1-182.
  12. [51]51. Newey, W. K. and D. McFadden (1994). Large sample estimation and hypothesis testing. In R. F. Engle and D. L. McFadden (Eds.), Handbook of Econometrics, Volume 4, Chapter 36, pp. 2111 -2245. Elsevier.
  13. [60]60. Xiao, Z., J. Shao, and M. Palta (2010). Instrumental variable and GMM estimation for panel data with measurement error. Statistica Sinica 20(4), 1725-1747.

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Related

Authors
Freyaldenhoven, S., Hansen, C., & Shapiro, J. M.
Affiliations
  • Federal Reserve Bank of Philadelphia (US)
  • University of Chicago (US)
  • National Bureau of Economic Research (US)
ORCID
Venue
American Economic Review
Publisher
American Economic Association
Published
2019-09-01
Volume
109
Issue
9
Pages
3307-3338
ISSN
0002-8282 (ISSN-L)
DOI
10.1257/aer.20180609
OpenAlex
W2799629285
Cited by
427
References
41
Citations by year
2026: 74 · 2025: 72 · 2024: 55 · 2023: 74 · 2022: 62 · 2021: 42 · 2020: 27 · 2019: 12 · 2018: 7 · 2017: 1 · 2016: 1
FWCI
28.15
Type
journal article
Language
en
Open access
Locations
  • American Economic Review (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Funders
National Science Foundation (1658037, 1558636)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Evaluation and Performance Assessment
Keywords
unmeasured confounding, causal identification, endogeneity, two-stage least squares, policy evaluation

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