Core Viewpoint - The article discusses the development of a real-time forecasting framework driven by large language models (LLMs) to predict macroeconomic indicators, addressing the inherent lag in traditional macroeconomic data collection and reporting processes [1][7]. Group 1: Real-time Forecasting Methods - Macroeconomic indicators typically experience delays due to the time-consuming data collection and validation processes, often resulting in the release of data in the following month or quarter [2][7]. - Three common methods for addressing the lag in macroeconomic data are outlined: 1. **Periodic Lagging Method**: Using previously published data, which is reliable but relies on linear extrapolation [8]. 2. **Dynamic Lagging Method**: Adjusting data based on historical release patterns, which also relies on linear extrapolation [8]. 3. **Real-time Forecasting Method**: Building models for real-time state predictions, which may introduce randomness [8]. Group 2: Specific Forecasting Techniques - The article details various forecasting techniques: 1. **High-Frequency Data Splitting**: Involves using dynamic high-frequency macro data to update low-frequency macro data predictions, exemplified by the GDPNow model. This method is interpretable but requires extensive domain knowledge and may lead to overfitting due to noise in high-frequency data [9]. 2. **SARIMAX Model**: A seasonal autoregressive integrated moving average model that incorporates seasonal parameters and exogenous variables to enhance predictive power. It is suitable for stable, high-frequency indicators with limited external shocks [10][14]. 3. **LLMs for Text Interpretation**: Utilizing LLMs to analyze unstructured text data (e.g., macro news, analyst reports) to generate predictive signals based on semantic relationships and logical reasoning. This method captures market reactions to sudden events more quickly than traditional models [3][15]. Group 3: Performance of Forecasting Models - The effectiveness of real-time forecasting methods is evaluated: 1. **Autoregressive Predictions**: Limited improvement in predictive accuracy for indicators with weak correlation to previous values, such as CPI month-on-month and new RMB loans. Strongly correlated indicators (≥0.8) can simply use lagged data without modeling [4][27]. 2. **LLMs Enhancements**: Significant improvements in predictive accuracy for various indicators when using LLMs, with notable increases in correlation for new RMB loans (from -0.1 to 0.9) and export amounts (from 0.37 to 0.72) [5][35]. Group 4: Conclusion and Recommendations - The article concludes with a recommended approach for real-time forecasting of lagging macroeconomic data: 1. For indicators with high correlation to previous values, use lagged data directly. 2. For stable indicators with weak trends, apply the SARIMAX model with seasonal adjustments. 3. Utilize LLMs in conjunction with news or report data for real-time predictions when other methods are unsuitable [45].