Sovereign Asset and Liability Management (SALM) and Efficient Debt Management: An Empirical Study for Jordan
Bibliographic record
Abstract
This research is about the effect of Sovereign Asset and Liability Management (SALM) on efficient debt management in Jordan using quarterly data from 2005 to 2023. The paper applies time series analysis methods, such as the Autoregressive Distributed Lag (ARDL) and Nonlinear Autoregressive Distributed Lag (NARDL) models to study the links between SALM components (cash reserves, foreign reserves, equity in state-owned enterprises, future revenues, government debt, fiscal expenditures and contingent liabilities) and Jordan's debt-to-GDP ratio. The results show that these variables have a significant impact on the short-term and long-term efficiency of debt management. Besides, the NARDL model shows that there are asymmetric impacts of equity in SOEs, future revenues and fiscal expenditures which means that these variables have different effects when they increase or decrease. The policy recommendations are to keep the debt levels sustainable, to accumulate foreign reserves, to manage contingent liabilities effectively and promote coordination among the relevant institutions for fiscal sustainability 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".