New Phillips Curve with Alternative Marginal Cost Measures for Canada, the United States, and the Euro Area
Bibliographic record
Abstract
Recent research on the new Phillips curve (NPC) (e.g., Galí, Gertler, and López-Salido 2001a) gives marginal cost an important role in capturing pressures on inflation. In this paper we assess the case for using alternative measures of marginal cost to improve the empirical fit of the NPC. Following Sbordone (2000), we derive the aggregation factors when firms use Cobb-Douglas with overhead labour and constant elasticity of substitution (CES) technologies. We estimate the NPC for Canada, the United States, and the euro area. Our structural results indicate that: (i) ignoring aggregation factors gives implausibly large estimates of the duration of price stickiness—in this respect, the aggregation factors improve the fit of the NPC substantially; (ii) the marginal cost measures based on CES technology improve the fit of the NPC relative to the Cobb-Douglas technology, particularly for Canada and the euro area; (iii) for Canada, the backward-looking component of inflation is quite strong relative to the United States and the euro area; and (iv) the incorporation of open-economy considerations for Canada does not yield better estimates of the NPC.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".