Inefficient forecast narratives: A BERT-based approach

Alexander Foltas

No 6/2025, HWWI Working Paper Series from Hamburg Institute of International Economics (HWWI)

Abstract: This paper contributes to previous research on the efficient integration of forecasters' narratives into business cycle forecasts. Using a Bidirectional Encoder Representations from Transformers (BERT) model, I quantify 19,300 paragraphs from German business cycle reports (1998-2021) and use them to predict the direction of consumption forecast errors. By testing the model on an evaluation sample, I find a highly significant correlation of modest strength between predicted and actual sign of the forecast error. The correlation coefficient is substantially higher for 12.8% of paragraphs with a predicted class probability of 85% or higher. By qualitatively reviewing 150 of such high-probability paragraphs, I find recurring narratives correlated with consumption forecast errors. Underestimations of consumption growth often mention rising employment, increasing wages and transfer payments, low inflation, decreasing taxes, crisis-related fiscal support, and reduced relevance of marginal employment. Conversely, overestimated consumption forecasts present opposing narratives. Forecasters appear to particularly underestimate these factors when they disproportionately affect low-income households.

Keywords: Macroeconomic forecasting; Evaluating forecasts; Business cycles; Consumption forecasting; Natural language processing; Language Modeling; Machine learning; Judgmental forecasting (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:hwwiwp:334496

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