Contribution to the Earlier Estimations of Quarterly National Accounts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Quarterly national accounts provide short-term macroeconomic information matched with those of the annual accounts. Their mission is to provide synthetic information as soon as possible after the end of the quarter. Due to the pressure, caused by Eurostat shortening deadlines on publishing this information, the task is more of creating a methodology to be used in the Czech Republic. Based on faster and more effi cient approaches, it should enable to perform and present estimates of aggregates of quarterly national accounts for the last quarter (and forecasts for the current quarter) at a satisfactory level of reliability. Basic considerations on the methods of quarterly estimation should therefore depend on indirect methods, ie., on mathematical and statistical models, that enable (given there is a system of short survey estimates) to accelerate and shorten publishing. The article off ers an original methodology of estimating quarterly national accounts values, based on time series analysis and presents the results on the data of national accounts of the Czech Republic.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.005 |
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 it