Daniel Aromi
Daniel Heymann

We propose a method to generate “synthetic surveys” that shed light on policymakers’ perceptions and narratives. This exercise is implemented using 80 time-stamped Large Language Models (LLMs) fine-tuned with FOMC meetings’ transcripts. Given a text input, finetuned models identify highly likely responses for the corresponding FOMC meeting. We evaluate this tool in three different tasks: sentiment analysis, evaluation of transparency in Central Bank communication and characterization of policymaking narratives. Our analysis covers the housing bubble and the subsequent Great Recession (2003-2012). For the first task, LLMs are prompted to generate phrases that describe economic conditions. The resulting output is verified to transmit policymakers’ information regarding macroeconomic and financial dynamics. To analyze transparency, we compare the content of each FOMC minutes to content generated synthetically through the corresponding fine-tuned LLM. The evaluation suggests the tone of each meeting is transmitted adequately by the corresponding minutes. In the third task, we show LLMs produce insightful depictions of evolving policymaking narratives. Thisanalysis reveals relevant narratives’ features such as goals, perceived threats, identified macroeconomic drivers, categorizations of the state of the economy and manifestations of emotional states.