Bibliographic record
Abstract
Abstract Empirical evidence linking exports to economic growth has been mixed and inconclusive. This study re‐examines the export‐led growth (ELG) hypothesis for Canada by testing for Granger causality from exports to national output growth using vector error correction models (VECM) and the augmented vector autoregressive (VAR) methodology developed in Toda and Yamamoto (1995) . Application of recent developments in time series modelling and the inclusion of relevant variables omitted in previous studies help to clarify the contradictory results from prior studies on the Canadian economy. The empirical results suggest that a long‐run steady state exists among the model's six variables and that Granger causal flow is unidirectional from real exports to real GDP. JEL Classification: F43, C32 Est‐ce que l’hypothèse de la croissance engendrée par les exportations est valide pour le Canada? Les résultats empiriques des études tentant de montrer le lien entre les exportations et la croissance économique sont mixtes et ne contiennent pas de résultats concluants. Cet article ré‐examine l’hypothèse dans le cas du Canada en testant la causalitéà la Granger pour les exportations et la croissance du produit national, en utilisant les modèles VECM et la méthodologie VAR développés par Toda et Yamamoto (1995) . L’application de certains développements récents dans l’analyse des séries chronologiques et l’inclusion de variables pertinentes omises dans les études antérieures aident à mettre de l’ordre dans les résultats contradictoires des études antérieures portant sur l’économie canadienne. Les résultats empiriques suggèrent qu’un régime permanent existe entre les six variables du modèle et que la causalitéà la Granger est unidirectionnelle des exportations réelles vers le PIB réel.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".