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• W2919838861 abstract "Abstract 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." @default.
• W2919838861 created "2019-03-11" @default.
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• W2919838861 date "2019-12-01" @default.
• W2919838861 modified "2023-09-23" @default.
• W2919838861 title "Let the Data Speak? On the Importance of Theory-Based Instrumental Variable Estimations" @default.
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• W2919838861 doi "https://doi.org/10.1111/geer.12192" @default.
• W2919838861 hasPublicationYear "2019" @default.
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