Inefficient forecast narratives: A BERT-based approach

Alexander Foltas

No 45, Working Papers from German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin

Abstract: I contribute 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 classify the signs of institutes' consumption forecast errors. The correlation is strong for 12.8% of paragraphs with a predicted class probability of 85% or higher. Reviewing 150 of such high-probability paragraphs reveals recurring narratives. 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; Judgemental forecasting (search for similar items in EconPapers)
JEL-codes: C53 E21 (search for similar items in EconPapers)
Date: 2024
New Economics Papers: this item is included in nep-big and nep-cmp
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:pp1859:300847

DOI: 10.18452/29133

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