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Cited by 1,324Spatial and Panel Data AnalysisAdvanced Causal Inference TechniquesStatistical Methods and Bayesian Inference

Inference with Difference-in-Differences and Other Panel Data

Donald, Stephen G, Lang, Kevin·2007·Review of Economics and Statistics
DOI 10.1162/rest.89.2.221
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Abstract

We examine inference in panel data when the number of groups is small, as is typically the case for difference-in-differences estimation and when some variables are fixed within groups. In this case, standard asymptotics based on the number of groups going to infinity provide a poor approximation to the finite sample distribution. We show that in some cases the t-statistic is distributed as t and propose simple two-step estimators for these cases. We apply our analysis to two well-known papers. We confirm our theoretical analysis with Monte Carlo simulations.

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  • Donald and Lang's approach has the disadvantage of the strong parametric assumption of homoskedastic Gaussian errors, which will often be hard to justify in practice.

    “The disadvantage is the strong parametric assumption of homoskedastic Gaussian errors, which will often be hard to justify in practice.”

    From What's trending in difference-in-differences? A synthesis of the recent econometrics literature (2023) · cites this paper as Donald and Lang (2007)

  • Donald and Lang's approach allows for valid inference when both the number of treated and untreated clusters is small.

    “A nice feature of this approach is that it allows for valid inference when both the number of treated and untreated clusters is small.”

    From What's trending in difference-in-differences? A synthesis of the recent econometrics literature (2023) · cites this paper as Donald and Lang (2007)

  • Donald and Lang (2007) show that one can obtain valid inference by using critical values from a t-distribution with J-2 degrees of freedom, where J is the total number of clusters.

    “They then show that one can obtain valid inference by using critical values from a t -distribution with J ´ 2 degrees of freedom, where J is the total number of clusters.”

    From What's trending in difference-in-differences? A synthesis of the recent econometrics literature (2023) · cites this paper as Donald and Lang (2007)

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Authors
Donald, S. G., & Lang, K.
Affiliations
  • The University of Texas at Austin (US)
  • Boston University (US)
ORCID
Lang, Kevin
Venue
Review of Economics and Statistics
Publisher
MIT Press
Published
2007-05
Volume
89
Issue
2
Pages
221-233
ISSN
  • 0034-6535 (ISSN-L)
  • 1530-9142
DOI
10.1162/rest.89.2.221
OpenAlex
W2154062944
Cited by
1,324
References
19
Citations by year
2026: 50 · 2025: 62 · 2024: 56 · 2023: 55 · 2022: 81 · 2021: 66 · 2020: 71 · 2019: 82 · 2018: 81 · 2017: 70 · 2016: 83 · 2015: 104 · 2014: 93 · 2013: 77 · 2012: 62
FWCI
112.32
Type
journal article
Language
en
Open access
closed
Locations
  • The Review of Economics and Statistics (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
Keywords
difference-in-differences, panel data, finite-sample inference, fixed effects, two-step estimator, asymptotic approximation, Monte Carlo simulation

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