Bibliographic record
Abstract
Model-based forecasts of important economic variables are part of the range of information considered for monetary policy decision making. Since some of the data underpinning these forecasts can be revised over time as new information is released, having access to the data that are available when decisions are made can have a significant impact on assessments of forecasting models. A database of published information for a set of money and credit variables has been developed at the Bank of Canada. This real-time database, which will make available estimates of money and credit data that have been published at different times, is expected to be of great help to researchers developing models based on money and credit data. The authors describe the contents of the new database and discuss patterns in data revisions. While they find that most revisions are unbiased, they provide evidence that revisions to some of the money and credit aggregates are biased. In particular, revisions to long-term business credit and total business credit tend to show an upward bias over longer periods. The authors argue that this may be because there tends to be a delay in factoring the effects of financial innovations into time series. Practitionners should consider this when interpreting developments in business credit.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".