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Inference in Difference-in-Differences: How Much Should We Trust in Independent Clusters?

Bruno Ferman

arXiv 4 Sep 2019 · Econometrics · publishedJournal of Applied Econometrics (2023) · 15 citations (OpenAlex)

arXiv:1909.01782 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We analyze the challenges for inference in difference-in-differences (DID) when there is spatial correlation. We present novel theoretical insights and empirical evidence on the settings in which ignoring spatial correlation should lead to more or less distortions in DID applications. We show that details such as the time frame used in the estimation, the choice of the treated and control groups, and the choice of the estimator, are key determinants of distortions due to spatial correlation. We also analyze the feasibility and trade-offs involved in a series of alternatives to take spatial correlation into account. Given that, we provide relevant recommendations for applied researchers on how to mitigate and assess the possibility of inference distortions due to spatial correlation.

Citation extraction

53
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100
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8,422
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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Barrios, T., Diamond, R., Imbens, G. W., and Kolesar, M (2012) Clustering, spatial correlations, and randomization inference0.8434375%
2Bertrand, M., Duflo, E., and Mullainathan, S (2004) How much should we trust differences-in-differences estimates?0.84333100%
3Pesaran, M. H (2006) Estimation and inference in large heterogeneous panels with a multifactor error structure0.7375340%
4Müller, U. K. and Watson, M. W (2021) Spatial Correlation Robust Inference0.6445240%
5Abadie, A., Athey, S., Imbens, G. W., and Wooldridge, J (2017) When should you adjust standard errors for clustering?0.6445240%
6de Chaisemartin, C. and D'Haultfoeuille, X (2018) Two-way fixed effects estimators with heterogeneous treatment effects0.6443267%
7Conley, T. G. and Taber, C. R (2011) Inference with Difference in Differences with a Small Number of Policy Changes0.64422100%
8Ferman, B. and Pinto, C (2019) Inference in differences-in-differences with few treated groups and heteroskedasticity self0.64422100%
9Roth, J (2022) Pre-test with caution: Event-study estimates after testing for parallel trends0.58510230%
10Müller, U. K. and Watson, M. W (2022) Spatial Correlation Robust Inference in Linear Regression and Panel Models0.5757229%

Showing the top 10 of 53 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Inference in Difference-in-Differences with Few Treated Units and Spatial Correlation0.73733
2ASSESSING INFERENCE METHODS0.40511
3Imputation of Counterfactual Outcomes when the Errors are Predictable0.40511
4ON THE USE OF DESIGN-BASED SIMULATIONS0.40511