Arbeitspapier
Pitfalls when Estimating Treatment Effects Using Clustered Data
Inference for estimates of treatment effects with clustered data requires great care when treatment is assigned at the group level. This is true for both pure treatment models and difference-in-differences regressions. Even when the number of clusters is quite large, cluster-robust standard errors can be much too small if the number of treated (or control) clusters is small. Standard errors also tend to be too small when cluster sizes vary a lot, resulting in too many false positives. Bootstrap methods generally perform better than t-tests, but they can also yield very misleading inferences in some cases.
- Sprache
-
Englisch
- Erschienen in
-
Series: Queen's Economics Department Working Paper ; No. 1387
- Klassifikation
-
Wirtschaft
Statistical Simulation Methods: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
- Thema
-
CRVE
grouped data
clustered data
panel data
wild cluster bootstrap
difference-in-differences
DiD regression
- Ereignis
-
Geistige Schöpfung
- (wer)
-
MacKinnon, James G.
Webb, Matthew D.
- Ereignis
-
Veröffentlichung
- (wer)
-
Queen's University, Department of Economics
- (wo)
-
Kingston (Ontario)
- (wann)
-
2017
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:42 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.
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 D.
- Queen's University, Department of Economics
Entstanden
- 2017
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