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

Author

Listed:
  • Grossmann Volker

    (University of Fribourg,Fribourg, Switzerland)

  • Osikominu Aderonke

    (University of Hohenheim,Stuttgart, Germany)

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.

Suggested Citation

  • Grossmann Volker & Osikominu Aderonke, 2019. "Let the Data Speak? On the Importance of Theory-Based Instrumental Variable Estimations," German Economic Review, De Gruyter, vol. 20(4), pages 831-851, December.
    • Handle: RePEc:bpj:germec:v:20:y:2019:i:4:p:e831-e851
    DOI: 10.1111/geer.12192
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    Keywords

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    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation
    • O11 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Macroeconomic Analyses of Economic Development

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