Non-linear adjustment in law of one price deviations and physical characteristics of goods
Bibliographic record
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 5-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 process of aggregation of heterogeneous components (”aggregation bias” of Imbs, et al. 2005).
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".