欧洲央行-使用ChatGPT增强GDP即时预报：PMInews发布的新应用（英）

Working Paper Series Enhancing GDP nowcasts with ChatGPT: a novel application of PMI news releases Gabe J. de Bondt, Yiqiao Sun Disclaimer: This paper should not be reported as representing the views of the European Central Bank (ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. No 3063 AbstractThis study involves tasking ChatGPT with classifying an “activity sentiment score” based on PMI newsreleases. It explores the predictive power of this score for euro area GDP nowcasting. We find that the PMItext scores enhance GDP nowcasts beyond what is embedded in ECB/Eurosystem Staff projections andEurostat’s first GDP estimate. The ChatGPT-derived activity score retains its significance in regressionsthat also include the composite output PMI diffusion index. GDP nowcasts are significantly enhanced withPMI text scores even when accounting for methodological variations, excluding extraordinary economicevents like the pandemic, and for different GDP growth quantiles. However, the forecast gains from theenhancement of GDP nowcasts with ChatGPT scores are time dependent, varying by calendar years.Sizeable forecast gains of on average about 20% were obtained apart from the two most recent years dueto exceptionally low forecast errors of the two benchmarks, especially the first GDP estimate.Keywords Chat Generative Pre-training Transformer, text analysis, zero-shot sentiment analysis,Purchasing Managers’ Index (PMI), nowcasting GDPJEL Classification C8; E32; C22ECB Working Paper Series No 30631Non-technical summary This study introduces a novel approach to economic forecasting by utilizing artificial intelligence (AI), specifically ChatGPT, to enhance predictions of euro area Gross Domestic Product (GDP). Traditional methods of economic forecasting primarily rely on numerical data, such as hard data on industrial production and retail sales as well as soft data such as Purchasing Managers’ Index (PMI) diffusion indices. Instead, this research explores the potential of integrating qualitative information - textual content from PMI news releases - into existing forecasts. What makes this study unique is its focus on the narrative, tone, and anecdotes reported in PMI news releases. ChatGPT was employed to analyse PMI news releases and generate activity sentiment scores. These scores quantify the sentiment about activity expressed in the narratives and anecdotes of the news release, ranging from strongly expanding activity to significantly contracting activity. The study then integrates these ChatGPT-derived activity scores in traditional GDP nowcasts, i.e., forecasts of real GDP growth in the current quarter, to assess their predictive power. The analysis utilizes two notoriously hard-to-beat benchmarks for GDP nowcasting, setting a high standard for accuracy, namely ECB/Eurosystem Staff projections and the first GDP estimate. The first benchmark includes judgment from experts and the second