Content-Based Metric on Monetary Policy Uncertainty by Using Large Language Models
17 Pages Posted: 10 Dec 2024
Abstract
Policy uncertainty is a potential source for reducing policy effectiveness. This study introduces a new method for measuring different types of policy uncertainty in news content using large language models (LLMs). We fine-tune the LLMs to identify different types of uncertainty expressed in newspaper articles based on their context, even if they do not contain specific keywords indicating uncertainty that existing studies have measured. By applying this method to Japan’s monetary policy from 2015 to 2016, we demonstrate that our approach successfully captures the dynamics of monetary policy uncertainty, which vary significantly depending on the type of uncertainty examined.
Keywords: Bank of Japan, Central Bank Communication, Generative Pre-trained Transformer, Large Language Model, Monetary Policy, Policy Uncertainty, text data
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