Managing a Country’s Sustainabilty - The Case of Malaysia and Indonesia Public Debt
Bibliographic record
Abstract
The study is driven by the motivation to examine the effects of policy interest rates and crude oil prices on Malaysian and Indonesian government borrowing within the framework of Keynesian macroeconomic theory. Using Autoregressive Distributed Lag (ARDL) model as an estimation tool over the observed period from March 2013 till June 2018, the study uncovers the absence of long-term equilibrium relationship between government borrowings and the two explanatory variables. However, based upon Error Correction Representation via ARDL model, there is a significant long-run relation (at 10% level) between Indonesian government borrowing and the two tested variables. Interestingly, this is not the case for Malaysia over both long-run and short-run relations. With respect to the short-run dynamics, there is a unidirectional causality running from crude oil price to Indonesian government borrowing. It seems crude oil price plays a significant role in influencing Indonesian government’s choice of public financing. As expected, the short-term policy rate has no significant bearing on government borrowings at all.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".