通过使用BERT和Web scraping进行NLP增强的通胀测量
Martin Berki1, Vanesa Andicsova1, Milos Oravec1
1Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, Bratislava, Slovakia.
Frontiers in artificial intelligence
|April 24, 2025
概括
这项研究引入了一种使用自然语言处理 (NLP) 和网络抓取来测量通货膨胀的新方法. 关税价格指数紧密跟踪协调消费者价格指数 (HICP),同时揭示了详细的市场波动.
科学领域:
- 计算经济学的计算经济学
- 自然语言处理自然语言处理.
- 计量经济学 计量经济学
背景情况:
- 传统的通货膨胀指标可能无法捕捉细分的市场动态.
- 协调消费者价格指数 (HICP) 作为一个关键的经济指标.
- 消费电子产品的价格表现出复杂的波动.
研究的目的:
- 使用NLP和网络抓取来开发一个定制的通胀指数.
- 为了将海关指数与官方HICP进行基准比较.
- 分析消费电子产品的详细价格变化.
主要方法:
- 网络扫描被用来收集消费电子产品的数据库.
- 用BERT模型将项目按COICOP类别进行高性能分类.
- 用权重和中位数的方法来构建定制价格指数.
主要成果:
- 在使用BERT将大约10,000个项目分为COICOP类别时获得了94.56%的准确性.
- 定制指数,特别是加权/中位数方法,与官方HICP紧密结合.
- 与HICP相比,在COICOP 091类别内观察到的月度通胀趋势的变化更大.
结论:
- NLP和网络抓取为替代通货膨胀测量提供了强大的方法.
- 计算方法可以通过提供更详细的见解来增强经济分析.
- 定制指数表明了对通货膨胀更细致的理解的潜力.
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