Arbeitspapier
Estimation, inference, and interpretation in the regression discontinuity design
The Regression Discontinuity Design (RDD) has proven to be a compelling and transparent research design to estimate treatment effects. We provide a review of the main assumptions and key challenges faced when adopting an RDD. We cover the most recent developments and advanced methods, and provide the key intuitions that underlie the statistical arguments. Among others, we summarize new insights that we consider to be highly relevant about the choice of bandwidth, optimal inference, discrete running variables, distributional effects, estimation in the presence of covariates, and the regression kink design. We also show how structural parameters can be estimated by combining an RDD identification strategy with theoretical models. We illustrate the procedures by applying them to data and we provide codes to replicate the results.
- Language
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Englisch
- Bibliographic citation
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Series: Discussion Papers ; No. 20-16
- Classification
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Wirtschaft
- Event
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Geistige Schöpfung
- (who)
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Melly, Blaise
Lalive, Rafael
- Event
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Veröffentlichung
- (who)
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University of Bern, Department of Economics
- (where)
-
Bern
- (when)
-
2020
- Handle
- Last update
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10.03.2025, 11:42 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Melly, Blaise
- Lalive, Rafael
- University of Bern, Department of Economics
Time of origin
- 2020
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Regression discontinuity design with covariates
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Another look at the regression discontinuity design