Measurement of Economic Forecast Accuracy: A Systematic Overview of the Empirical Literature
Bibliographic record
Abstract
The primary purpose of the paper is to enable deeper insight into the measurement of economic forecast accuracy. The paper employs the systematic literature review as its research methodology. It is also the first systematic review of the measures of economic forecast accuracy conducted in scientific research. The citation-based analysis confirms the growing interest of researchers in the topic. Research on economic forecast accuracy is continuously developing and improving with the adoption of new methodological approaches. An overview of the limits and advantages of the methods used to assess forecast accuracy not only facilitate the selection and application of appropriate measures in future analytical works but also contribute to a better interpretation of the results. In addition to the presented advantages and disadvantages, the chronological presentation of methodological development (measures, tests, and strategies) provides an insight into the possibilities of further upgrading and improving the methodological framework. The review of empirical findings, in addition to insight into existing results, indicates insufficiently researched topics. All in all, the results presented in this paper can be a good basis and inspiration for creating new scientific contributions in future works.
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 | 0.052 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.026 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".