Practical Applications of How Does the Fed Make Decisions: A Machine Learning Augmented Taylor Rule
Bibliographic record
Abstract
In <ext-link><bold><italic>How Does the Fed Make Decisions: A Machine Learning Augmented Taylor Rule</italic></bold></ext-link>, published in the Winter 2023 issue of <bold><italic>The Journal of Fixed Income</italic></bold>, authors <bold>Boyu Wu</bold>, <bold>Asawari Sathe</bold>, and <bold>Qian Wang</bold> of <bold>Vanguard</bold> and <bold>Amina Enkhbold</bold> of the <bold>Bank of Canada</bold> introduce a new four-factor, computer-learning model to predict the federal funds rate set by the Federal Open Market Committee (FOMC). The authors argue that their four-factor model, which considers inflation, labor market conditions, US financial market conditions, and commodity prices (as a proxy for global conditions), outperforms the Taylor rule for predicting the actions of the FOMC.
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How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".