Causal Nexus Real Effective Exchange Rate, External Debt, Liquidity and Stock Value: Case study on Canada: Vector Error Correction Model (VECM) Approach
Bibliographic record
Abstract
Abstract Canada has currently one of the largest economies in the world. However, the Economy of Canada has witnessed various boom and bust over the last several decades. Real GDP growth has recorded on upward trend growth from 1980’s until 2007. However, in the aftermath of the Global financial crisis, the GDP growth rate of Canada has fallen to just below 2 percent. During the same time period, the level of Canada’s external debt, real effective exchange rate and money supply has grown exponentially. Likewise, the expansion of capital market in Canada reinforced the robust economic expansion and caused upsurge in the level of stock traded. This research examines the causal relationship between real effective exchange rate, external debt, liquidity and stock value in Canada between 1980 until 2016. To check the stationarity of the data, the research conducted Augmented Dickey-Fuller unit roots test. The result shows that after taking their first difference all variables turned to become stationary. Based on Johansen tests for co-integration, the study variables are co-integrated in the long run, therefore the research used Vector Error Correction Model (VECM). Furthermore, to verify the suitability of the data, the research conducted diagnostic tests like normality test, la-grange multiplier test and Heteroskedasticity Test. The estimation rest shows that there is both long run and short run causality among the research variables. In the short run, Canada’s liquidity has short run causality on Real effective exchange rate and Stock values. Conversely, real effective exchange rate and Government Debt have short run causal effect on Canada’s liquidity level. Likewise, stock value traded and real effective exchange rate have short run casual effect on Canada’s government debt. In the long run, the VECM estimation revealed that there is a long run causal relationship running from liquidity, government debt and stock value towards the Canada’s Real effective exchange rate (REER).
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".