Real-Time Inflation Expectations in China: An Explainable Nowcasting Framework and a Reproducible Communication–Attention Pilot
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This paper develops an explainable framework for measuring real-time inflation expectations in China and embedding those signals in inflation nowcasting. The production design is LLM-ready: official texts, news narratives, and search data can be mapped into an economically structured taxonomy of inflation mechanisms. For transparent evaluation, however, the article implements a reduced-form public-data pilot rather than claiming a fully historical LLM deployment. The pilot combines monthly official CPI, PPI, M2 growth, and one-year LPR data for 2023M1–2025M12 with two reproducible expectation channels: a policy-communication index derived from PBOC materials and a lagged attention proxy that approximates public information demand under open-data constraints. In expanding-window nowcasts, expectation-augmented models improve on stricter macro-only linear benchmarks; the best full random-forest specification records an RMSE of 0.253, compared with 0.273 for a macro elastic-net and 0.359 for a simple macro linear model. Feature attribution shows that lagged CPI, PPI, and money growth remain the main anchors, while communication and the lagged attention proxy provide non-trivial incremental information. An exploratory policy-window exercise suggests that the same narrative environment tends to coincide with easing episodes, although that evidence is descriptive because the sample contains only five LPR cuts. The paper’s main contribution is therefore methodological and institutional: it shows how an LLM-ready narrative measurement system can be disciplined by transparent public data, explainable modeling, and cautious validation in China’s recent low-inflation regime.