We analyze the conditions in which ignoring spatial correlation is problematic for inference in differences-in-differences (DID) models. If the spatial correlation structure follows a linear factor model,  we show that inference ignoring such correlation remains reliable when either (i) the second moment of the difference between the pre- and post-treatment averages of common factors is low, or (ii) the distribution of factor loadings has the same expected values for treated and control groups , and do not exhibit significant spatial correlation . Simulations with real datasets corroborate these conclusions. We provide guidelines on how to minimize inference problems due to spatial correlation , and analyze the properties of pre-testing for spatial correlation problems .
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 .
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We consider a setting in which
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which allows for a rich variety of spatial correlation structures. In this setting,
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such correlation remains reliable when either
spatial correlation is more problematic when both
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low, or
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the same
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, and do not exhibit significant spatial correlation
. The choice among different estimators also affect the relevance of spatial correlation for inference
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We provide
These findings provide important
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, and analyze the properties of pre-testing for spatial correlation problems