Egy unortodox jegybanki adatrevízió háttere, utóélete és tanulságai
Bibliographic record
Abstract
In December 2016 the National Bank of Hungary (NBH), by referring to its "backcasts" (based on analyses of past data-revisions), arbitrarily revised upwards the data of the Hungarian Statistical Office (HSO) on GDP-growth for the first three quarters of 2016. This "methodological innovation" (as put by the NBH) served to support its unrealistic 2.8 percent GDP-growth projection for 2016. In March 2017, the HSO reported GDP to have grown by a mere 2 percent in 2016. Hence, the "backcast" of the NBH, combined with its forecast for the last quarter of 2016, proved to be a failure. However, the NBH did not give up: without offering a substantive explanation for its forecast error, it claimed in its 2017 March Inflation Report that GDP-growth was by 0.2 percentage points higher in 2016 than reported by the HSO. This mode of conduct of the NBH can be objected both on ethical and professional grounds. By this practice the NBH not only questions the reliability of official statistical data, but it also makes its forecasts incomparable both across time and with those of other analysts. While expressing uncertainty regarding future data-revisions by a central bank certainly makes sense, there are no professional arguments to support a practice whereby a central bank simply overwrites official data by its extremely uncertain "backcasts".
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.079 | 0.037 |
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".