Forecasting Inflation Using Economic Narratives

主讲人：

孟令超，北京大学经济学院金融系博士生

时间：

2024年2月29日周四

12:00-13:30

地点：

北京大学经济学院101会议室

摘要：

We use economic narratives to forecast inflation with a large news corpus and machine learning algorithms. The economic narratives from the full text content of over 880,000 Wall Street Journal articles are decomposed into multiple time series representing interpretable news topics, which are then used to predict inflation. The results indicate that narrative-based forecasts are more accurate than the benchmarks, especially during recession periods. Narrative-based forecasts perform better in long-run forecasting, and provide incremental predictive information even after controlling macroeconomic big data. In particular, information about inflation expectations and prices of specific goods embedded in narratives contributes to their predictive power. Overall, we provide a novel representation of economic narratives and document the important role of economic narratives in inflation forecasting.

供稿单位：经济学院金融系

供稿人：孟令超