Arbeitspapier

Inference with Difference-in-Differences Revisited

A growing literature on inference in difference-in-differences (DiD) designs with grouped errors has been pessimistic about obtaining hypothesis tests of the correct size, particularly with few groups. We provide Monte Carlo evidence for three points: (i) it is possible to obtain tests of the correct size even with few groups, and in many settings very straightforward methods will achieve this; (ii) the main problem in DiD designs with grouped errors is instead low power to detect real effects; and (iii) feasible GLS estimation combined with robust inference can increase power considerably whilst maintaining correct test size – again, even with few groups.

Sprache
Englisch

Erschienen in
Series: IZA Discussion Papers ; No. 7742

Klassifikation
Wirtschaft
Hypothesis Testing: General
Estimation: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Thema
difference in differences
hypothesis test
power
cluster robust
feasible GLS

Ereignis
Geistige Schöpfung
(wer)
Brewer, Mike
Crossley, Thomas F.
Joyce, Robert
Ereignis
Veröffentlichung
(wer)
Institute for the Study of Labor (IZA)
(wo)
Bonn
(wann)
2013

Handle
Letzte Aktualisierung
10.03.2025, 11:44 MEZ

Datenpartner

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Objekttyp

  • Arbeitspapier

Beteiligte

  • Brewer, Mike
  • Crossley, Thomas F.
  • Joyce, Robert
  • Institute for the Study of Labor (IZA)

Entstanden

  • 2013

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