The Relationship between Current Account Deficits and Growth in Montenegro: ARDL Bounds Testing Approach
Bibliographic record
Abstract
Abstract In this study, we investigate the relationship between current account deficits and growth in Montenegro by applying the bounds testing (ARDL) approach to co-integration for the period from the third quarter of 2011 to the last quarter of 2016. The bounds tests suggest that the variables of interest are bound together in the long run when growth is the dependent variable. The results also confirm a bidirectional long run and short run causal relationship between current account deficits and growth. The short run results mostly indicate a negative relationship between changes in the current account deficit GDP ratio and the GDP growth rate. This means that any increase of the value of independent variable (current account deficit GDP ratio) will result in decrease of the rate of GDP growth and vice versa. The long-run effect of the current account deficit to GDP ratio on GDP growth is positive. The constant (β0) is positive but also the (β1), meaning that with the increase of CAD GDP ratio of 1 measuring unit, the GDP growth rate would grow by 0,5459. This positive and tight correlation could be explained by overlapping structure of the constituents of CAD and the drivers of GDP growth (such as tourism, energy sector, agriculture etc.). The results offer new perspectives and insights for new policy aiming for sustainable economic growth of Montenegro.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".