Arbeitspapier
Using the Statistical Concept of "Severity" to Assess Seemingly Contradictory Statistical Evidence (with a Particular Application to Damage Estimation)
When parties present divergent econometric evidence, the court may view such evidence as contradictory and thus ignore it completely, without conducting closer analysis. We develop a simple method for distinguishing between actual and merely apparent contradiction based on the statistical concept of the "severity" of the furnished evidence. Again using "severity", we also propose a method for reconciling divergent findings in instances of mere seeming contradiction. Our chosen application is that of damage estimation in follow-on cases.
- Language
-
Englisch
- Bibliographic citation
-
Series: LawFin Working Paper ; No. 3
- Classification
-
Wirtschaft
- Subject
-
Cartel damages
severity
statistical testing
- Event
-
Geistige Schöpfung
- (who)
-
Bönisch, Peter
Inderst, Roman
- Event
-
Veröffentlichung
- (who)
-
Goethe University, Center for Advanced Studies on the Foundations of Law and Finance (LawFin)
- (where)
-
Frankfurt a. M.
- (when)
-
2020
- DOI
-
doi:10.2139/ssrn.3702906
- Handle
- URN
-
urn:nbn:de:hebis:30:3-616436
- Last update
-
10.03.2025, 11:42 AM CET
Data provider
This object is provided by:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the 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
- Bönisch, Peter
- Inderst, Roman
- Goethe University, Center for Advanced Studies on the Foundations of Law and Finance (LawFin)
Time of origin
- 2020
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Statistical methods to assess gene-environment : interaction with surrogate genetic measures
Introduction to statistical inference
Index to statistical tables
Introduction to statistical physics
Statistical learning approaches to information filtering
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