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.

Language
Englisch

Bibliographic citation
Series: Queen's Economics Department Working Paper ; No. 1387

Classification
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
Subject
CRVE
grouped data
clustered data
panel data
wild cluster bootstrap
difference-in-differences
DiD regression

Event
Geistige Schöpfung
(who)
MacKinnon, James G.
Webb, Matthew D.
Event
Veröffentlichung
(who)
Queen's University, Department of Economics
(where)
Kingston (Ontario)
(when)
2017

Handle
Last update
10.03.2025, 11:42 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

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

Time of origin

  • 2017

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