Arbeitspapier

Trimmed Mean Group Estimation of Average Treatment Effects in Ultra Short T Panels under Correlated Heterogeneity

Under correlated heterogeneity, the commonly used two-way fixed effects estimator is biased and can lead to misleading inference. This paper proposes a new trimmed mean group (TMG) estimator which is consistent at the irregular rate of n 1/3 even if the time dimension of the panel is as small as the number of its regressors. Extensions to panels with time effects are provided, and a Hausman-type test of correlated heterogeneity is proposed. Small sample properties of the TMG estimator (with and without time effects) are investigated by Monte Carlo experiments and shown to be satisfactory and perform better than other trimmed estimators proposed in the literature. The proposed test of correlated heterogeneity is also shown to have the correct size and satisfactory power. The utility of the TMG approach is illustrated with an empirical application.

Sprache
Englisch

Erschienen in
Series: CESifo Working Paper ; No. 10725

Klassifikation
Wirtschaft
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
correlated heterogeneity
irregular estimators
two-way fixed effects
FE-TE
tests of correlated heterogeneity
calorie demand

Ereignis
Geistige Schöpfung
(wer)
Pesaran, M. Hashem
Yang, Liying
Ereignis
Veröffentlichung
(wer)
Center for Economic Studies and ifo Institute (CESifo)
(wo)
Munich
(wann)
2023

Handle
Letzte Aktualisierung
10.03.2025, 11:46 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

  • Pesaran, M. Hashem
  • Yang, Liying
  • Center for Economic Studies and ifo Institute (CESifo)

Entstanden

  • 2023

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