This short note addresses a serious problem in combining macro (country-level) time series data, e.g., oil price uncertainty (OPU) or economic/trade policy uncertainty (EPU/TPU), with micro (firm-level) financial and accounting panel data, e.g., corporate leverage, investment, and innovation, to name a few. In most of the applications, the main interest is to assess the impacts of country-level explanatory variable on the firm-level dependent variable, with year fixed effects (along with other firm fixed effects, etc.) being included. Since the macro time series are the same for all firms in each year, it is straightforward to show that the macro time series variable is perfectly correlated with the year fixed effects, and thus unidentifiable. We employ three real data sets to illustrate the perfect multicollinearity issue, and our demonstrations cast doubt on the findings of several existing studies suffering from this issue. Finally, we also offer some practical ways to get around with (at least mitigate) this problem.

这篇简短的说明解决了将宏观（国家层面）时间序列数据（例如油价不确定性 (OPU) 或经济/贸易政策不确定性 (EPU/TPU)）与微观（公司层面）财务和会计面板相结合的严重问题数据，例如企业杠杆、投资和创新，仅举几例。在大多数应用程序中，主要兴趣是评估国家级解释变量对公司级因变量的影响，其中包括年份固定效应（以及其他公司固定效应等）。由于每年所有公司的宏观时间序列都是相同的，因此很容易证明宏观时间序列变量是完全相关的具有年份固定效应，因此无法识别。我们使用三个真实数据集来说明完美的多重共线性问题，并且我们的演示对存在此问题的几项现有研究的结果提出了质疑。最后，我们还提供了一些实用的方法来解决（至少缓解）这个问题。