This highly accessible book presents robustness testing as the methodology for conducting quantitative analyses in the presence of model uncertainty.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Eric Neumayer is Professor of Environment and Development and Pro-Director Faculty Development at the London School of Economics and Political Science (LSE).
Inhaltsangabe
1. Introduction Part I. Robustness - A Conceptual Framework: 2. Causal complexity and the limits to inferential validity 3. The logic of robustness testing 4. The concept of robustness 5. A typology of robustness tests 6. Alternatives to robustness testing? Part II. Robustness Tests and the Dimensions of Model Uncertainty: 7. Population and sample 8. Concept validity and measurement 9. Explanatory and omitted variables 10. Functional forms beyond default 11. Causal heterogeneity and context conditionality 12. Structural change as temporal heterogeneity 13. Effect dynamics 14. Spatial correlation and dependence 15. Conclusion.
1. Introduction Part I. Robustness - A Conceptual Framework: 2. Causal complexity and the limits to inferential validity 3. The logic of robustness testing 4. The concept of robustness 5. A typology of robustness tests 6. Alternatives to robustness testing? Part II. Robustness Tests and the Dimensions of Model Uncertainty: 7. Population and sample 8. Concept validity and measurement 9. Explanatory and omitted variables 10. Functional forms beyond default 11. Causal heterogeneity and context conditionality 12. Structural change as temporal heterogeneity 13. Effect dynamics 14. Spatial correlation and dependence 15. Conclusion.
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