AI 中文总结
研究在非平稳环境下微观经济异质性对预测总体通货膨胀的作用，设计自适应机器学习流程，将微观预测与基准预测结合，应用于英国微观数据，发现微观数据对预测总体通货膨胀有价值，尤其在大冲击后。
AI 中文摘要
在非平稳环境中，微观经济异质性有助于预测总体通货膨胀吗？我们开发了一种扫描测试，用于判断在起点和持续时间未知的区间内一个预测是否优于另一个预测。为利用扫描测试检测到的偶然预测能力，我们设计了一个自适应机器学习流程。将价格变化分布编码为高维向量，与梯度提升树算法结合，再通过自适应算法将微观预测与其他基准预测相结合，仅在微观预测表现良好时使用。应用于英国微观数据有四个主要结果：微观预测仅在2020年后的波动期优于单变量基准；扫描测试能检测微观预测优势期并纳入组合预测；组合预测在2020年前与单变量基准相当，2020年后各期限表现更好；微观数据对组合预测的价值在2020年后显现。我们得出结论，微观数据对预测总体通货膨胀有价值，但仅在大冲击之后。
英文摘要
Does microeconomic heterogeneity help to forecast aggregate inflation in a non-stationary environment? We develop a scan test for whether one forecast outperforms another, over an interval with unknown starting point and duration. To exploit any occasional forecasting power that the scan test detects, we design an adaptive machine learning pipeline. We encode the distribution of price changes into a high-dimensional vector, which we combine with a gradient boosted trees algorithm. We then combine this micro forecast with other benchmark forecasts, using an adaptive algorithm that makes use of the micro forecast only when it performs well. We apply the pipeline to UK microdata, with four main results. First, the micro forecast outperforms a univariate benchmark, but only in the volatile period after 2020. Second, the scan test detects periods of micro outperformance, so the micro forecast enters the combined forecast. Third, the combined forecast performs comparably to the univariate benchmark before 2020 and better at every horizon after 2020. Fourth, the value of microdata for the combined forecast materializes after 2020. We conclude that microdata are valuable for forecasting aggregate inflation, but only after large shocks.
Comments187 pages, 26 figures, 45 tables
