Let the Data Speak? On the Importance of Theory‐Based Instrumental Variable Estimations

Volker Grossmann and Aderonke Osikominu

German Economic Review, 2019, vol. 20, issue 4, e831-e851

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.

Date: 2019
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https://doi.org/10.1111/geer.12192

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Journal Article: Let the Data Speak? On the Importance of Theory-Based Instrumental Variable Estimations (2019)
Working Paper: Let the Data Speak? On the Importance of Theory-Based Instrumental Variable Estimations (2019)
Working Paper: Let the Data Speak? On the Importance of Theory-Based Instrumental Variable Estimations (2019)
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