Economics at your fingertips  

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

Bruno Ferman

Papers from

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.

Date: 2019-09, Revised 2022-09
New Economics Papers: this item is included in nep-ecm
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link) Latest version (application/pdf)

Related works:
Working Paper: Inference in Differences-in-Differences: How Much Should We Trust in Independent Clusters? (2019) Downloads
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

Access Statistics for this paper

More papers in Papers from
Bibliographic data for series maintained by arXiv administrators ().

Page updated 2022-09-12
Handle: RePEc:arx:papers:1909.01782