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.

中文翻译：

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让数据说话？基于理论的工具变量估计的重要性

摘要在没有随机对照实验的情况下，鉴定通常是通过工具变量（IV）策略进行的，通常是两阶段最小二乘估计。但是，根据贝叶斯法则，在假设为真的事例发生概率较低的情况下（例如，排除的工具与内生回归变量部分相关），估计结果的解释可能会存在根本性的缺陷。本文认为，严格的理论推理是设计可靠的识别策略的关键，最重要的是寻找有效工具的候选人。我们讨论了宏观开发文献中突出的IV分析，以说明结构派生IV方法的潜在好处。