Evaluación del sesgo en las estimaciones de Contabilidad Nacional Trimestral: Estudio de las añadas en España /Assessing Quarterly Spanish National Accounts Estimates. A Study of the vintages
Bibliographic record
Abstract
El objetivo de esta aportación es analizar la bondad de las estimaciones elaboradas en el marco Contabilidad Nacional Trimestral (CNTR) de España sobre la evolución del PIB, con detalle a tres ramas productivas, medida a través de sus tasas interanuales e intertrimestrales. En concreto, se evaluará la calidad y precisión de las estimaciones mediante el análisis de la posible existencia de discrepancias sistemáticas entre los avances (primera estimación) de las tasas de variación de un determinado trimestre y las sucesivas estimaciones de ese mismo trimestre de referencia. The aim of this paper is to analyze the accuracy of estimates elaborated in the Quarterly Spanish National Accounts (QSNA) regarding the evolution of the main economic variables. In particular, we assess the quality and precision of the estimates by analyzing the possible existence of systematic discrepancies between the advances (first estimate) of a certain quarter and the successive estimates published for this same reference quarter.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".