Bibliographic record
Abstract
Technological innovations in the financial industry pose major problems for the measurement of monetary aggregates. The authors describe work on a new measure of money that has a more satisfactory means of identifying and removing the effects of financial innovations. The new method distinguishes between the measured data (currency and deposit balances) and the underlying phenomena of interest (the intended use of money for transactions and savings). Although the classification scheme used for monetary aggregates was originally designed to provide a proxy for the phenomena of interest, it is breaking down. The authors feel it is beneficial to move to an explicit attempt to measure an index of intended use. The distinction is only a preliminary step. It provides a mechanism that allows for financial innovations to affect measured data without fundamentally altering the underlying phenomena being measured, but it does not automatically accommodate financial innovations. To achieve that step will require further work. At least intuitively, however, the focus on an explicit measurement model provides a better framework for identifying when financial innovations change the measured data. Although the work is preliminary, and there are many outstanding problems, if the approach proves successful it will result in the most fundamental reformulation in the way money is measured since the introduction of monetary aggregates half a century ago. The authors review previous methodologies and describe a dynamic factor approach that makes an explicit distinction between the measured data and the underlying phenomena. They present some preliminary estimates using simulated and real data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".