Arbeitspapier
Wild Bootstrap for Fuzzy Regression Discontinuity Designs: Obtaining Robust Bias-Corrected Confidence Intervals
This paper develops a novel wild bootstrap procedure to construct robust bias-corrected (RBC) valid confidence intervals (CIs) for fuzzy regression discontinuity designs, providing an intuitive complement to existing RBC methods. The CIs generated by this procedure are valid under conditions similar to the procedures proposed by Calonico et al. (2014) and related literature. Simulations provide evidence that this new method is at least as accurate as the plug-in analytical corrections when applied to a variety of data generating processes featuring endogeneity and clustering. Finally, we demonstrate its empirical relevance by revisiting Angrist and Lavy (1999) analysis of class size on student outcomes.
- Sprache
-
Englisch
- Erschienen in
-
Series: IZA Discussion Papers ; No. 12801
- Klassifikation
-
Wirtschaft
Semiparametric and Nonparametric Methods: General
Single Equation Models; Single Variables: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions
Single Equation Models: Single Variables: Instrumental Variables (IV) Estimation
- Thema
-
fuzzy regression discontinuity
robust confidence intervals
wild bootstrap
average treatment effect
- Ereignis
-
Geistige Schöpfung
- (wer)
-
He, Yang
Bartalotti, Otávio
- Ereignis
-
Veröffentlichung
- (wer)
-
Institute of Labor Economics (IZA)
- (wo)
-
Bonn
- (wann)
-
2019
- Handle
- Letzte Aktualisierung
-
10.03.2025, 11:43 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
- He, Yang
- Bartalotti, Otávio
- Institute of Labor Economics (IZA)
Entstanden
- 2019
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