Arbeitspapier

Wild Bootstrap Randomization Inference for Few Treated Clusters

When there are few treated clusters in a pure treatment or difference-in-differences setting, t tests based on a cluster-robust variance estimator (CRVE) can severely over-reject. Although procedures based on the wild cluster bootstrap often work well when the number of treated clusters is not too small, they can either over-reject or under-reject seriously when it is. In a previous paper, we showed that procedures based on randomization inference (RI) can work well in such cases. However, RI can be impractical when the number of possible randomizations is small. We propose a bootstrap-based alternative to randomization inference, which mitigates the discrete nature of RI P values in the few-clusters case. We also compare it to two other procedures. None of them works perfectly when the number of clusters is very small, but they can work surprisingly well.

Sprache
Englisch

Erschienen in
Series: Queen's Economics Department Working Paper ; No. 1404

Klassifikation
Wirtschaft
Hypothesis Testing: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Thema
CRVE
grouped data
clustered data
panel data
wild cluster bootstrap
difference-in-differences
DiD
randomization inference

Ereignis
Geistige Schöpfung
(wer)
MacKinnon, James G.
Webb, Matthew
Ereignis
Veröffentlichung
(wer)
Queen's University, Department of Economics
(wo)
Kingston (Ontario)
(wann)
2018

Handle
Letzte Aktualisierung
10.03.2025, 11:45 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • MacKinnon, James G.
  • Webb, Matthew
  • Queen's University, Department of Economics

Entstanden

  • 2018

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