Asymmetric output cost of lowering inflation: empirical evidence for Canada
Bibliographic record
Abstract
A strand of theoretical and empirical evidence in the literature suggests non‐linearity in the output‐inflation relationship, viz. a non‐linear Phillips curve. We develop a VAR model of output, inflation, and terms of trade augmented with logistic smooth transition autoregression specifications. Empirically, the model captures non‐linear features present in the data. Output costs of reducing inflation vary, depending on the economy, size of inflation change, and whether policy makers seek to disinflate or prevent inflation from rising. Thus, inferences based on the conventional linear Phillips curve may provide misleading signals about the cost of lowering inflation and the appropriate policy stance. JEL Classification: C32, E52 Le coût asymétrique en termes de production de la réduction de l’inflation: résultats pour le Canada. Ce mémoire a son origine dans les travaux qui suggèrent une certaine non linéarité dans la relation production‐inflation i.e. une courbe de Phillips qui serait non‐linéaire. Les auteurs utilisent un modèle VAR de la production, de l’inflation et des termes d’échange, enrichi de spécifications autogressives définissant une transition logistique souple. Il appert que les coûts en termes de perte de production de la réduction de l’inflation varient grandement selon l’état de l’économie, la taille des changements recherchés dans le taux d’inflation, et selon que les autorités cherchent créer une déflation ou simplement à empêcher l’accélération de l’inflation. Voilà qui implique que les inférences tirées d’un modèle construit sur la courbe linéaire traditionnelle de Phillips peuvent fournir des signaux trompeurs quant aux coûts de la réduction de l’inflation et suggérer de mauvaises politiques.
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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".