APPLIED COMPUTING → ECONOMICS
Prompting for Policy: Forecasting Macroeconomic Scenarios with Synthetic LLM Personas
This research investigates the effectiveness of using synthetic Large Language Model (LLM) personas for macroeconomic forecasting, specifically replicating the European Central Bank's Survey of Professional Forecasters. The study finds that while LLMs can achieve competitive accuracy comparable to human experts, persona descriptions offer no measurable advantage, suggesting that prompt engineering beyond basic context may not be necessary. The LLMs also exhibit remarkably lower dispersion in forecasts compared to human panels, indicating a consensus-seeking behavior despite diverse inputs.
Key Executive Impact
0%
LLM Forecast Accuracy vs. Human Experts
0
Measurable Advantage from Personas
1x
Dispersion of LLM Forecasts vs. Human
100%
Out-of-Sample Performance Maintained
The study suggests that robust data integration and model architecture are more critical than elaborate prompt engineering for LLM-based forecasting. This implies potential cost savings by simplifying prompt design without sacrificing accuracy, leading to more efficient deployment of AI-augmented forecasting systems in financial institutions and central banks. The ability to maintain performance on out-of-sample data highlights the potential for reliable, real-time economic analysis.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Key Research Insights
The research explores the application of LLMs in macroeconomic forecasting, specifically replicating the ECB Survey of Professional Forecasters. It highlights that while LLMs can match human expert accuracy, the complexity of prompt design (like persona descriptions) provides no additional benefit. This suggests a streamlined approach to prompt engineering can be effective, emphasizing data quality and model architecture over elaborate textual prompts. A notable finding is the significantly lower dispersion in LLM forecasts, pointing to a consensus-seeking behavior.
- LLMs achieve competitive forecasting accuracy similar to human experts.
- Persona descriptions provide no measurable forecasting advantage.
- GPT-40 maintains performance on out-of-sample data.
- LLM forecasts show significantly lower dispersion than human forecasts.
0.01
Smallest MAE difference for HICP (Out-of-sample CY)
Enterprise Process Flow
PersonaHub Corpus (370M)
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Multi-stage Filtering (2368 relevant personas)
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Simulate ECB SPF Responses (50 rounds, 4 variables, 4 horizons)
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Generate 118,400 AI Forecasts
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Compare against Human Experts & Realized Outcomes
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Evaluate Accuracy & Persona Contribution
| Forecaster Type | Key Characteristic | Dispersion Level |
|---|---|---|
| AI Personas | Near-zero disagreement (0.000-0.050) | Highly Homogeneous |
| Human Experts | Broader disagreement (0.177-0.700) | Diverse Panel Consensus |
Case Study: Ablation Experiment: Persona Effect
Key Statistic: No measurable forecasting advantage from persona descriptions.
Challenge: To determine if sophisticated persona descriptions significantly improve LLM forecasting accuracy.
Solution: Conducted a controlled ablation experiment comparing forecasts with and without persona descriptions for 100 baselines (5,000 forecasts).
Outcome: Statistical tests (t-test, Kolmogorov-Smirnov) showed no significant difference in error distributions, indicating persona descriptions can be omitted to reduce computational costs without sacrificing accuracy (t = -1.02, p = 0.31; D = 0.05, p = 0.28).
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