Trade Openness and Economic Growth in Canada: An Evidence from Time-Series Tests
Bibliographic record
Abstract
This study examines the effects of international trade and investment on output and tests the null hypothesis of Granger non-causality among trade, investment and economic growth in Canada. The long-run model is estimated using several single-equation and system estimators to assess the robustness of results across methodologies. The single-equation, OLSEG, GMM, DOLS, NLLS and FMOLS, estimates of the model provide consistent support for the positive and significant long-run effects of exports and investment on output. The ML system estimates cross-validate the cointegrating relationship and reinforce the positive effects of exports and investment and the negative effects of imports on output. The over-parameterized level-VAR estimates suggest unidirectional Granger-causality from exports, imports and investment each to output. The estimates of the model with structural breaks support the long-run relationship, though the evidence is not unambiguous ubiquitously across all the tests. The evidence supporting the positive and significant long-run effects overwhelms the evidence providing weak or no support for the effects of trade on output. The results underline the need for the acceleration of exports (and investment) to offset the demand-reducing effects of imports and escalate the altitudes of output and economic growth.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".