论文标题
机器学习对通货膨胀驱动力有何评论？
What does machine learning say about the drivers of inflation?
论文作者
论文摘要
本文通过一种简单但计算密集的机器学习技术的镜头研究了CPI通胀的驱动力。更具体地说，它可以预测2000年至2021年之间20个发达国家的通货膨胀率依赖于基于六个关键宏观经济变量建造的1,000棵回归树。这种不可知论的，纯粹的数据驱动方法可（相对）良好的结果预测性能。从样本根均方根误差（RMSE）中，即使是样本中的基准计量经济学模型也会击败。通货膨胀期望对CPI结果的部分影响也会在本文中引起。总体而言，结果突出了发达经济体对通货膨胀结果的期望的作用，尽管它们的重要性在过去十年中似乎有所下降。
This paper examines the drivers of CPI inflation through the lens of a simple, but computationally intensive machine learning technique. More specifically, it predicts inflation across 20 advanced countries between 2000 and 2021, relying on 1,000 regression trees that are constructed based on six key macroeconomic variables. This agnostic, purely data driven method delivers (relatively) good outcome prediction performance. Out of sample root mean square errors (RMSE) systematically beat even the in-sample benchmark econometric models. Partial effects of inflation expectations on CPI outcomes are also elicited in the paper. Overall, the results highlight the role of expectations for inflation outcomes in advanced economies, even though their importance appears to have declined somewhat during the last 10 years.
