Une nouvelle méthode d'estimation de l'écart de production et son application aux États-Unis, au Canada et à l'Allemagne
Bibliographic record
Abstract
This study introduces a new method for identifying the output gap, based on the estimation of multivariate autoregression (VAR) models. This approach, which involves using restrictions to identify structural shocks that have only a transitory effect on output but that affect the trend inflation rate, is compared with the decomposition method proposed by Blanchard and Quah (1989). We also show some applications based on VAR model estimations for the Canadian, United States, and German economies. We find in particular that shocks having a transitory influence on output can explain most of the variance in inflation in these three countries. This suggests that the long-term neutrality hypothesis about the production of inflation-influencing shocks is a good approximation. We also find that only a small part of the output variance reflects changes in trend inflation. We go on to estimate simple inflation indicator models in order to assess the information that is contained in output gap estimations and used to explain inflationary pressures. The results suggest that the proposed method is a promising one.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".