Multivariate Temporal Disaggregation
Bibliographic record
Abstract
The 2007-2009 global financial and economic crisis led to a reflection on the need for an international agreed system of high frequency macroeconomic statistics and indicators.At the time it was recognised that high frequency statistics and indicators based on an international agreed methodology could facilitate the early detection of changes in macroeconomic conditions.Providing relevant accurate reliable and timely data, e.g. based on so-called rapid estimates, is therefore of the essence to facilitate the monitoring and assessment of policies.Such data and analytical gaps were extensively discussed in a series of international seminars (held in 2009 and 2010) jointly organised by United Nations Statistics Division (UNSD) and Eurostat in cooperation with Statistics Canada, Statistics Netherlands (CBS), and the Russian Federal State Statistics Service (Rosstat) and with participation of a broad range of stakeholders across the statistical, the analytical and policy domains.During the discussions at the seminars, it emerged that-on the one hand-there were large differences across countries in the timeliness of key macroeconomic indicators.On the other hand, a consensus emerged that guidance for the compilation of rapid estimates should be prepared based on best international practices.In addition, it was deemed necessary to clarify the terminology associated with rapid estimates.In order to establish a common understanding of rapid estimates, Eurostat took the lead in drafting this handbook and preparing of a glossary of terms for rapid estimates to clarify the different typologies of rapid estimates, their purposes and characteristics.The handbook presented herewith outlines practical and suitable compilation methods for rapid estimates.It draws on a wide range of experience and expertise and benefits from recent theoretical and practical developments in the area.The handbook is intended to assist those producing rapid estimates e.g. in the area of key short term macroeconomic indicators.It is also intended to assist countries that plan to set up a more comprehensive system of rapid estimates by providing both methodological foundations for their compilation and by giving practical guidance on individual steps and elements of the underlying compilation process.This handbook should be considered as both a reference tool, stating the state of the art in the area of rapid estimates and a guide towards the implementation of rapid estimate systems in organisations.It has been designed to meet the requirements of a wide audience, both technical and non-technical, be it academics, research bodies, private institutions or Government entities.It is therefore an invaluable tool and highly recommended for anyone wanted to develop a better understanding of rapid estimates.The Handbook benefited from comments from experts who participated in the Expert Review, namely (in alphabetical order of countries followed by international organizations): Zsuzsanna Szõkéné Boros (Hungarian Central Statistical Office, Hungary), Luciana Crosilla and Solange Leproux (Italian
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.010 |
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".