We analyze the conditions in which ignoring spatial correlation is problematic for inference in differences-in-differences (DID) models. We consider a setting in which the spatial correlation follows a linear factor model, which allows for a rich variety of spatial correlation structures. In this setting, we show that inference ignoring spatial correlation is more problematic when both (i) the second moment of the difference between the pre- and post-treatment averages of common factors is large, and (ii) the distribution of factor loadings has different expected values for treated and control groups. The choice among different estimators also affect the relevance of spatial correlation for inference  . Simulations with real datasets corroborate these conclusions. These findings  provide important guidelines on how to minimize inference problems due to spatial correlation.
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.
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(DID)
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consider a setting in which the spatial correlation follows a linear factor model, which allows for a rich variety
show that the relevance
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structures. In this setting, we show that inference ignoring spatial correlation is more problematic when both (i) the second moment of the difference between the pre- and post-treatment averages of common factors is large, and (ii) the distribution of factor loadings has different expected values for treated and control groups. The choice among different estimators also affect the relevance of spatial correlation
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(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
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provide a better understanding on when spatial correlation should be more problematic, and