The NIRCU and the Phillips curve: an approach based on micro data
Bibliographic record
Abstract
In this paper we propose a straightforward method to derive a non-inflationary rate of capacity utilization (NIRCU) based on micro data.We condition the current capacity utilization of firms on their current and planned price adjustments.The noninflationary capacity utilization rate is then defined as the rate where a firm feels no price adjustment pressure.One of the main advantages is that this methodology uses structural aspects and does not make it necessary to operate with -often rather arbitrary -statistical filters.We show that our aggregate NIRCU performs remarkably well as an indicator of inflationary pressure in a Phillips curve estimation.JEL classification: E31, E32, E52 Le NIRCU et la courbe de Phillips : une approche ancrée dans les micro-données.Dans ce mémoire, on propose une méthode simple pour dériver le taux non-inflationniste d'utilisation de la capacité (NIRCU) à partir de micro-données.On établit l'utilization de la capacité des firmes sur la base des ajustements (présents ou anticipés) de prix.Le taux non-inflationniste d'utilisation de la capacité est alors défini comme celui o ù la firme ne ressent aucune pression pour ajuster ses prix.L'un des principaux avantages de cette approche est que cette méthodologie utilise les aspects structurels et ne nécessite pas qu'on utilise des filtres statistiques souvent arbitraires.On montre que le NIRCU agrégé performe relativement bien en tant qu'indicateur de la pression inflationniste dans la calibration de la courbe de Phillips.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".