Journal of Econometrics· 2026Q1
Clustering with potential multidimensionality: Inference and practice
- 1citations
- Q1SCImago
- 2026year
Short summary
A new variance estimator is proposed for M-estimation that refines conservative procedures and remains valid when clustering standard errors in one or more dimensions due to sampling or assignment uncertainty.
AI-generated from the title and abstract; the full text is not read.
Key points
- Proposes a new variance estimator for M-estimation with multidimensional clustering.
- The estimator refines a conservative procedure to ensure validity, unlike existing methods.
- Addresses uncertainty from sampling or assignment in clustering standard errors.
- Provides a design-based interpretation for common empirical clustering practices.
AI-generated from the title and abstract; the full text is not read.
Abstract
We show how clustering standard errors in one or more dimensions can be justified in M-estimation when there is sampling or assignment uncertainty. Since existing procedures for variance estimation are either conservative or invalid, we propose a variance estimator that refines a conservative procedure and remains valid. We then interpret environments where clustering is frequently employed in empirical work from our design-based perspective and provide insights on their estimands and inference procedures.
The authors' abstract, as published at the source. Journal of Econometrics, 2026 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Artificial Intelligence
Artificial IntelligenceComputer Science