Enhancing Inflation Nowcasting with LLM: Sentiment Analysis on News
This work addresses inflation forecasting for economists and policymakers, but it is incremental as it builds on existing models with a small enhancement.
This study tackled the problem of improving inflation nowcasting during volatile periods like the COVID-19 pandemic by integrating sentiment analysis from news using a fine-tuned BERT model, resulting in a marginal accuracy improvement in the Cleveland Fed's model.
This study explores the integration of large language models (LLMs) into classic inflation nowcasting frameworks, particularly in light of high inflation volatility periods such as the COVID-19 pandemic. We propose InflaBERT, a BERT-based LLM fine-tuned to predict inflation-related sentiment in news. We use this model to produce NEWS, an index capturing the monthly sentiment of the news regarding inflation. Incorporating our expectation index into the Cleveland Fed's model, which is only based on macroeconomic autoregressive processes, shows a marginal improvement in nowcast accuracy during the pandemic. This highlights the potential of combining sentiment analysis with traditional economic indicators, suggesting further research to refine these methodologies for better real-time inflation monitoring. The source code is available at https://github.com/paultltc/InflaBERT.