Let the Data Speak? On the Importance of Theory-Based Instrumental Variable Estimations
Volker Grossmann () and
No 7469, CESifo Working Paper Series from CESifo Group Munich
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, aforemost 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.
Keywords: Bayes’ rule; economic development; identification; instrumental variable estimation; macroeconomic theory (search for similar items in EconPapers)
JEL-codes: C10 C36 O11 (search for similar items in EconPapers)
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Working Paper: Let the Data Speak? On the Importance of Theory-Based Instrumental Variable Estimations (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:ces:ceswps:_7469
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