Patents and citations are powerful tools increasingly used in financial economics (and management research more broadly) to understand innovation. Biases may result, however, from the interactions between the truncation of patents and citations and the changing composition of inventors. When aggregated at the firm level, these patent and citation biases can survive popular adjustment methods and are correlated with firm characteristics. These issues can lead to problematic inferences. We provide an actionable checklist to avoid biased inferences and also suggest machine learning as a potential new way to address these problems.

中文翻译：

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专利数据的使用和滥用：财务及其他方面的问题

专利和引用是金融经济学（以及更广泛的管理研究）中越来越多地用于理解创新的强大工具。然而，专利和引用的截断与发明人组成的变化之间的相互作用可能会导致偏差。当在公司层面汇总时，这些专利和引用偏差可以在流行的调整方法中幸存下来，并与公司特征相关。这些问题可能会导致有问题的推论。我们提供了一个可操作的清单，以避免有偏见的推断，并建议将机器学习作为解决这些问题的潜在新方法。