Bibliographic record
Abstract
Trade agreements do not necessitate business cycle comovement. Focusing on NAFTA, we investigate whether business cycles in Canada, Mexico, and the US have become more synchronous after the landmark trade agreement came into effect in 1994. To this end, using the newly-developed Hamilton filter, we decompose the real GDPs of the three countries to derive their business cycle components; then, we conduct time-difference analyses, which illuminate correlations at different time intervals, to study business cycle synchronization. We find that business cycles in Mexico and the US have become positively correlated after NAFTA—they were weakly and negatively correlated during the pre-NAFTA period. Contrastingly, correlations amongst the US and Canadian business cycles have weakened during the post-NAFTA period; nevertheless, these two countries' business cycles continue to be tightly and positively correlated. The oft-used Hodrick-Prescott filter is utilized to confirm the robustness of the results—the two filters lead to similar conclusions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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; both teacher heads agree on what is shown here.
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".