Investigating Export Determinants: A Time Series Evidence From Canada
Bibliographic record
Abstract
Export is an important macroeconomic factor that can elevate a country’s output performance and raise employment opportunities, in any economy. Any country may expand the number of its allies through exports. Foundation trade theories, like absolute advantage and comparative advantage, suggest that a country should export the product with greater absolute or comparative advantage. This sheds light on allocating the optimal resources for producing low-price products and flouting the idea of specialization among the countries of the world. The present study explores the factors that may influence the export performance of a developed economy like Canada from 1979 to 2019. The study findings provide evidence of the absence of multicollinearity and that the data series for the selected functional form of the study is stationary at mixed order. The results of the ARDL bounds test confirm long-run cointegrating relations between exports and its determinants for Canada. The results further reveal that per capita energy consumption and government final consumption expenditures significantly elevate export performance in both the long and short run, while population size significantly elevates exports performance only in the long run in Canada. Moreover, the findings also expose that real effective exchange rate significantly reduces exports in both the long and short run in Canada: This means that by depreciating Canadian currency, Canadian exports will be boosted. The real interest rate reports a negative but insignificant impact on the Canadian export function in both the long and short run. Finally, the CUSUM and CUSUM Square graphs confirm the stability of the estimated coefficients for the Canadian export function for the selected sample of the study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".