Bibliographic record
Abstract
Purpose The literature mostly investigates the impact of trade and financial integration on business cycle synchronization. The author differs by focusing on the real effective exchange rate as the target variable in the North American Free Trade Agreement (NAFTA) region. In particular, the author investigates synchronization by analyzing the short- and long-run dynamics of the real effective exchange rates of Canada, Mexico and the US for 2008–2019. Design/methodology/approach The author first employs stationarity and cointegration tests to specify and estimate the long-run equilibrium relation between the real effective exchange rates of Canada, Mexico and the US. The author then specifies and estimates an error-correction model for each real effective exchange rate in order to investigate whether the adjustment in eliminating disequilibrium is asymmetric. Findings The results indicate that the real effective exchange rates of Canada, Mexico and the US are cointegrated with only one long-run equilibrium relation. Canada's real effective exchange rate responds symmetrically to eliminate both negative and positive disequilibrium with a similar speed of adjustment. However, the response of Mexico's real effective exchange rate is asymmetric, as it responds to eliminate only positive disequilibrium. The US real effective exchange rate does not respond to disequilibrium, perhaps because it has a large economy with much stronger competition beyond the NAFTA region than both Canada and Mexico. Originality/value This is the first study that investigates real effective exchange rate synchronization in the NAFTA region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".