A Local Projections Approach to Difference‐in‐Differences
ABSTRACT We propose a local projections (LPs)‐based difference‐in‐differences (DiD) approach that subsumes many of the recent solutions proposed in the literature to address possible biases arising from negative weighting. We combine LPs with a flexible “clean control” condition to define appropriate sets of treated and control units. Our proposed LP‐DiD estimator can be implemented with various weighting and normalization schemes for different target estimands, can be extended to include covariates or accommodate nonabsorbing treatment, and is simple and fast to implement. A simulation and two empirical applications demonstrate that the LP‐DiD estimator performs well in common applied settings.
Authors
- Òscar Jordà (ORCID: https://orcid.org/0000-0002-9588-2354)
- Daniele Girardi (ORCID: https://orcid.org/0000-0002-3423-3174)
- Alan M. Taylor (ORCID: https://orcid.org/0000-0003-3273-0844)
- Arindrajit Dubé
Institutions
- King's College London (GB)
- Federal Reserve Bank of San Francisco (US)
- University of Massachusetts Amherst (US)
- International Society of Automation (US)
- Centre for Economic Policy Research (GB)
- Bank of England (GB)
- Columbia University (US)
- University of California, Davis (US)
Publication Details
- Journal
- Journal of Applied Econometrics
- Published
- 2025-07-19
- DOI
- https://doi.org/10.1002/jae.70000
- Citations
- 21
- Primary Topic
- Advanced Causal Inference Techniques
- Type
- article
- Field-Weighted Citation Impact
- 55.23
Funders
- Università degli Studi di Milano
- King's College London
- Università di Pisa
- University of California, Davis
- University of Massachusetts Amherst