Drivers, trends, and dynamic interactions in long-run sectoral relationships: evidence from Canada
Bibliographic record
Abstract
This study examines the cointegration among Canadian economic sectors, identifies the primary drivers of long-run relationships, and analyses both long-run Granger and instantaneous causality, particularly during four major economic crises. It employs Johansen’s trace test, the simulated trace test with structural breaks, and Hansen and Johansen’s recursive trace test. A moving average representation is also used to analyse the common stochastic trends. Additionally, we apply a vector error correction model-based Granger causality analysis, an error correction-based causality test, instantaneous causality analysis, and generalized impulse response functions. The results indicate that the sectors are in long-run equilibrium, with integration increasing since the global financial crisis. Notably, the utilities, energy, commercial services, financials, real estate, consumer staples, and materials sectors emerge as key drivers leading all the sectors towards long-run equilibrium. We observe strong long-term Granger causal relationships and instantaneous causality, suggesting that price information transmission occurs through both short-term and long-term channels. These findings offer valuable implications: investors can leverage these insights for arbitrage and portfolio optimization, while policymakers can use them for risk mitigation and price forecasting enhancement.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".