Artikel
Replication to assess statistical adequacy
"Statistical adequacy" is an important prerequisite for securing reliable inference in empirical modelling. This paper argues for more emphasis on replication that specifically assesses whether the results reported in empirical studies are based on statistically adequate models, i.e., models with valid underpinning statistical assumptions that pass relevant diagnostic tests for misspecification. A replication plan is briefly outlined to illustrate what this would involve in practice in the context of a specific study by Acemoglu, Gallego and Robinson (Institutions, human capital, and development, Annual Review of Economics, 2014).
- Language
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Englisch
- Bibliographic citation
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Journal: Economics: The Open-Access, Open-Assessment E-Journal ; ISSN: 1864-6042 ; Volume: 12 ; Year: 2018 ; Issue: 2018-60 ; Pages: 1-16 ; Kiel: Kiel Institute for the World Economy (IfW)
- Classification
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Wirtschaft
Multiple or Simultaneous Equation Models: Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
Multiple or Simultaneous Equation Models: Instrumental Variables (IV) Estimation
Education and Economic Development
Capitalist Systems: Property Rights
Economic Development: General
- Subject
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Replication
statistical adequacy
reliability of inference
science trustworthy-ness
misspecification testing
instrumental variables
reduced form
fundamental determinants of economic development
- Event
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Geistige Schöpfung
- (who)
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Owen, P. Dorian
- Event
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Veröffentlichung
- (who)
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Kiel Institute for the World Economy (IfW)
- (where)
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Kiel
- (when)
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2018
- DOI
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doi:10.5018/economics-ejournal.ja.2018-60
- Handle
- Last update
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10.03.2025, 11:45 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
- Artikel
Associated
- Owen, P. Dorian
- Kiel Institute for the World Economy (IfW)
Time of origin
- 2018