Information transferred from interest rate to monetary aggregates
Bibliographic record
Abstract
This dataset presents the quantified information flow from interest rates to money demand using Rényi transfer entropy. Four monetary aggregates (M1, M1ADJ, NewM1, and Divisia M1) and two opportunity cost measures (3-month Treasury bill, and Divisia user-cost) are considered. The information flows are calculated over short intervals (for the stationary purpose) of length 35, 40, and 45 quarters. For the definition of time series and their relevant time spans, please refer to the paper. This dataset can serve as an indicator of the effectiveness of monetary policy decisions. It would be appreciated if use of the dataset could be acknowledged as follows: Movaghari, H., Serletis, A. and Sermpinis, G. (2024). Money demand stability: New evidence from transfer entropy, International Economics, .
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.379 |
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; both teacher heads agree on what is shown here.
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".