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

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  • Bruno Ferman

Abstract

We analyze the conditions in which ignoring spatial correlation is problematic for inference in differences-in-differences models. We show that the relevance of spatial correlation for inference (when it is ignored) depends on the amount of spatial correlation that remains after we control for the time- and group-invariant unobservables. As a consequence, details such as the time frame used in the estimation, and the choice of the estimator, will be key determinants on the degree of distortions we should expect when spatial correlation is ignored. Simulations with real datasets corroborate these conclusions. These findings provide a better understanding on when spatial correlation should be more problematic, and provide important guidelines on how to minimize inference problems due to spatial correlation.

Suggested Citation

  • Bruno Ferman, 2019. "Inference in Differences-in-Differences: How Much Should We Trust in Independent Clusters?," Papers 1909.01782, arXiv.org, revised Sep 2020.
  • Handle: RePEc:arx:papers:1909.01782
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    1. Bruno Ferman & Cristine Pinto, 2019. "Inference in Differences-in-Differences with Few Treated Groups and Heteroskedasticity," The Review of Economics and Statistics, MIT Press, vol. 101(3), pages 452-467, July.
    2. MacKinnon, James G. & Webb, Matthew D., 2020. "Randomization inference for difference-in-differences with few treated clusters," Journal of Econometrics, Elsevier, vol. 218(2), pages 435-450.
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    Cited by:

    1. James G. MacKinnon & Matthew D. Webb, 2020. "When and How to Deal with Clustered Errors in Regression Models," Working Paper 1421, Economics Department, Queen's University.
    2. Bruno Ferman, 2020. "Inference in Differences-in-Differences with Few Treated Units and Spatial Correlation," Papers 2006.16997, arXiv.org, revised Feb 2021.

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    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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