Nonlinear Adjustment in Law of One Price Deviations and Physical Characteristics of Goods
Bibliographic record
Abstract
Abstract At a level of individual goods, heterogeneity of marginal transaction costs, proxied by price‐to‐weight ratios and stowage factors, explains a large part of the variation in thresholds of no‐adjustment and conditional half‐lives of law of one price deviations. Prices of heavier (more voluminous) goods deviate further before becoming mean‐reverting. Moreover, after becoming mean‐reverting, prices of heavier goods converge more slowly. Together with measures of pricing power, market size, distance, and exchange rate volatility, these factors explain up to 43% of variation in no‐adjustment threshold estimates across 52 goods in US–Canada post‐Bretton Woods monthly CPI data and are robust in a broader five‐country dataset. They open two avenues for the importance of marginal transaction costs in accounting for real exchange rate persistence: through (a) generating persistence in individual real exchange rate components, and (b) accentuating it by the aggregation of heterogeneous components (“aggregation bias” of Imbs et al., 2005a ).
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".