News-Based Inflation Expectations: LLM-Assisted Measurement and Forecasting

We develop a news-based inflation expectations index for Thailand using a scalable workflow that integrates topic modeling, LLM-assisted labeling, and fine-tuned BERT classification. Based on 1.1 million Thai-language news articles from 2015–2024, the index leads both headline inflation and firm inflation expectations. Given that inflation narratives in news are inherently subjective and often ambiguous, we show that prompt design can materially affect downstream economic inference. In out-ofsample forecasting, augmenting autoregressive benchmarks with the news index reduces RMSE by up to 32% for headline inflation and 30% for firm inflation expectations, with gains increasing at longer horizons. SHAP-based decomposition reveals a horizon-dependent information structure: price-specific topics drive short-term forecasts, while macroeconomic narratives dominate at longer horizons. Our findings demonstrate that LLM-assisted text analysis can generate economically meaningful inflation indicators in non-English, emerging-economy settings. The index also performs particularly strong during periods of elevated inflation uncertainty.