Menu Costs, Relative Prices, and Inflation: Evidence for Canada
Bibliographic record
Abstract
Authors' note: Subsequent to completing this Working Paper, we realized that the way we constructed the weighted relative prices, ri, as described on page 8, is not invariant to the rate of inflation and this introduces a bias in favour of the menu-cost hypothesis. Preliminary results with a correction to this problem reveal that it affects the quantitative results. We are currently revisiting our empirical analysis more completely to consider the quantitative and qualitative implications of removing this bias. The menu-cost models of price adjustment developed by Ball and Mankiw (1994;1995) predict that short-run movements in inflation should be positively related to the skewness and the variance of the distribution of disaggregated relative-price shocks in each period. We test these predictions on Canadian data using the distribution of changes in disaggregated producer prices to measure the skewness and standard deviation of relative-price shocks. We find the Canadian data, both in the context of partial correlations and standard price Phillips curve equations, are highly supportive of the predictions that arise from the menu-cost models. Indeed, we find that the positive relationship between inflation and the skewness of the distribution of relative-price shocks is one of the most robust features of the Canadian Phillips curve and significantly improves our ability to explain inflation dynamics.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.010 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".