Bibliographic record
Abstract
Within the framework of a common monetary policy, the monitoring of economic developments in the euro area, on a regular basis, is of particular importance. Despite of the ongoing improvement, the data available for the euro area as a whole are still relatively limited and released with some lag. The assessment of the economic situation requires synthetic measures representative of activity in the economy as a whole. Gross Domestic Product (GDP) is the best measure acknowledged for this purpose. However, GDP is only made available on a quarterly basis and released with a significant lag, which makes it difficult to assess economic activity on a regular and timely basis. In fact, the first estimate for the euro area GDP in a given quarter is released 70 days after the end of that quarter.(1) Thus, one needs to resort to other synthetic measures which provide information on economic developments in the euro area on a more timely and frequent basis. The purpose of this article is to evaluate the performance of several economic composite indicators, which are currently released on a regular basis by several institutions, including the European Commission, the Organisation for Economic Co-operation and Development (OECD) and the Centre for Economic Policy Research (CEPR). The aim of this article is to assess to what extent these composite indicators allow the monitoring of GDP growth. For this purpose, we resort both to time and frequency domain analysis. This article is organised as follows. Section 2 makes a brief description of the methodology used to evaluate the composite indicators. Section 3 presents the main features of the indicators released by the different institutions and makes an overall assessment of their performance. Section 4 addresses other issues regarding the practical use of the indicators and section 5 concludes.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".