Let the Data Speak? On the Importance of Theory-Based Instrumental Variable Estimations
BP2-STS
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Grossmann, Volker
ORCID
University of Fribourg, Fribourg, Switzerland; CESifo, Munich , Germany; IZA, Bonn , Germany; CReAM, London , United Kingdom
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Osikominu, Aderonke
University of Hohenheim, Stuttgart, Germany; IAB, New York, United States of America; CESifo, Munich, Germany; IZA, Bonn, Germany; CEPR, London, United Kingdom
Published in:
- German Economic Review. - Walter de Gruyter GmbH. - 2019, vol. 20, no. 4, p. e831-e851
English
In absence of randomized-controlled experiments, identification is often aimed via instrumental variable (IV) strategies, typically two-stage least squares estimations. According to Bayes’ rule, however, under a low ex ante probability that a hypothesis is true (e.g. that an excluded instrument is partially correlated with an endogenous regressor), the interpretation of the estimation results may be fundamentally flawed. This paper argues that rigorous theoretical reasoning is key to design credible identification strategies, the foremost, finding candidates for valid instruments. We discuss prominent IV analyses from the macro-development literature to illustrate the potential benefit of structurally derived IV approaches.
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Faculty
- Faculté des sciences économiques et sociales et du management
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Department
- Département d'économie politique
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Language
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Classification
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Economics
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License
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Rights reserved
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Open access status
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green
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Identifiers
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Persistent URL
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https://folia.unifr.ch/unifr/documents/322914
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