时 间：2023年5月22日 10:00-11:30
地 点：理科大楼A1514；
腾讯会议ID：666-852-935
报告人：姜富伟中央财经大学教授
主持人：石芸华东师范大学副教授
摘 要：
In this paper we apply economic narratives to inflation forecasting using a large news corpus and machine learning algorithms. We measure economic narratives quantitatively from the full text content of over 880,000 Wall Street Journal articles and represent them as interpretable news topics. The results indicate that narrative-based forecasts are more accurate than the benchmarks both in-sample and out-of-sample, which perform especially well during recession periods. Narrative-based forecasts perform better in the long-run forecasting, suggesting that narratives help to capture the slowly-varying trend inflation objectives. Information about inflation expectations and prices of specific goods embedded in narratives contributes to its predictive power. Overall, we provide a novel representation of economic narratives and highlight the important role of economic narratives in inflation forecasting.
报告人简介：
姜富伟，中央财经大学教授、博导，金融工程系主任，国家社科基金重大项目首席专家，黄大年教学团队核心成员，北京市海淀区政协委员，目前主要关注数字经济与金融科技相关交叉研究，在Journal of Financial Economics、Review of Financial Studies、Management Science、Journal of Econometrics、《管理世界》、《金融研究》、《经济学季刊》、《管理科学学报》等发表论文50余篇，被评为ESI经济管理类全球前1%最高被引用论文、RFS最高被引用论文、JFE最高被引用论文等，国家自然科学基金考核评价“特优”，获《金融研究》优秀论文奖、国际金融管理协会最佳论文奖、亚洲金融协会最佳论文奖、中国金融工程学年会优秀论文奖、金融图书金羊奖等奖励荣誉。学术观点被《哈佛商业评论》、《清华金融评论》、CCTV、澎湃新闻等转载，担任多本中英文学术期刊的编委和副主编和国家自然科学基金、国家社科基金、教育部、中国人民银行等科研基金与人才计划评审专家。