Impact of Trump’s Phenomenon, Brexit, & Oil Prices Fluctuation Toward Indonesia’s Economic Growth
Bibliographic record
Abstract
This paper tries to find impact of global uncertainties toward Indonesia’s economic growth. Several problems which will be discussed in this paper namely: impacts of President Donald Trump’s policies, Brexit, and uncertainty regarding crude oil prices. It conducted from 1st quarter of 2010 until 1st quarter of 2017. The method of analysis used here is VECM (Vector Error Correction Model). We use dummy variable to capture the specific change of economic policies when Brexit and Trump’s emergence appear as the major issues which attract attention around the world. We consider these as the uncertainties which influence global society. Based on the result, there is positive impact of economic policy uncertainty in UK in the long-run. When Brexit was taken into account, in the short-run, it also has positive impact toward Indonesia’s economic growth. Meanwhile economic policy uncertainty in the US generates negative impact on Indonesia’s economic growth. But Trump’s emergence in the US presidency produces positive impact in the short-run. Oil price fluctuation as the latest shock in the global context has positive significant impact on Indonesia’s economic growth. We consider these results as ways to find breakthrough in understanding of changing policies from developed countries; that not all of them will contribute to negative matters. The conjecture, hunch, and any speculation must be postponed due to lack of convincing proofs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".