The common sources of business cycles in Trans‐Pacific countries and the US? A comparison with <scp>NAFTA</scp>
Bibliographic record
Abstract
Abstract This paper uses both a non‐structural and a structural approach to investigate the drivers of the business cycles in the US and 15 Trans‐Pacific (TP) countries. Our non‐structural analysis, based on a principal component methodology, reveals the shares of variation in macroeconomic variables that are due to factors common to both the US and the TP region, and factors that are region‐specific. We obtain similar measures by using a structural model (an estimated two‐country dynamic stochastic general equilibrium model) that allows for common and correlated shocks across the two regions. The clear and common finding from our analyses is that common shocks explain a substantial amount of macroeconomic variation. Comparison with the NAFTA region, along this dimension, reveals that the US economy is more similar to the TP region (a wider region that also includes Mexico and Canada) than its two neighbours.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".