Inefficient forecast narratives: A BERT-based approach - Alexander Foltas (Humboldt University Berlin, 2024)
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.
urn:nbn:de:kobv:11-110-18452/29758-1
Keywords: E21; C53; 330; Macroeconomic forecasting; Evaluating forecasts; Business cycles; Consumption forecasting; Natural language processing; Language Modeling; Machine learning; Judgemental forecasting