We consider the problem of inference in Differences-in-Differences models when there are few treated units and errors are spatially correlated. We first show that, when there is a single treated unit, existing inference methods designed for settings with few treated and many control units remain asymptotically valid when errors are strongly mixing. However, these methods are invalid with more than one treated unit. We propose asymptotically valid, though generally conservative, inference methods for settings with more than one treated unit. Our findings are relevant even for settings in which the number of treated units is usually considered as large enough to rely on clustered robust standard errors.
We consider the problem of inference in Differences-in-Differences models when there are few treated units and errors are spatially correlated. We first show that, when there is a single treated unit, existing inference methods designed for settings with few treated and many control units remain asymptotically valid when errors are strongly mixing. However, these methods are invalid with more than one treated unit. We propose asymptotically valid, though generally conservative, inference methods for settings with more than one treated unit. These alternative inference methods are valid even when the relevant distance metric across units is unavailable. Moreover, they may be relevant alternatives even for settings in which the number of treated units is usually considered as large enough to rely on clustered robust standard errors.
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Our findings are relevant
These alternative inference methods are valid even when the relevant distance metric across units is unavailable. Moreover, they may be relevant alternatives