Mixed-Frequency Bayesian Predictive Synthesis for Economic Nowcasting
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We develop a novel framework for dynamic modelling of mixed-frequency data using Bayesian predictive synthesis. The proposed framework—unlike other mixed-frequency methods—considers data reported at different frequencies as latent factors, in the form of predictive distributions, which are dynamically synthesized and updated to produce coherent forecast distributions. Time-varying biases and interdependencies between data reported at different frequencies are learnt and effectively mapped onto easily interpretable parameters with associated uncertainty. Furthermore, the proposed framework allows for flexible methodological specifications based on policy goals and utility. A macroeconomic study of nowcasting two decades of quarterly US GDP using monthly macroeconomic and financial indicators is presented. In terms of both point and density forecasts, our proposed method significantly outperforms competing methods throughout the quarter, and is competitive with the aggregate Survey of Professional Forecasters. The study further shows that incorporating information during a quarter, and sequentially updating information throughout, markedly improves the performance, while providing timely insights that are useful for decision-making.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it