Shock Response Analysis of Indonesian Macroeconomic Variables
Bibliographic record
Abstract
This study aims to analyze the shock response between the variables of economic growth, consumption, investment, government spending, export, poverty, unemployment and income inequality in all provinces in Indonesia during 2015-2021.This research is important because promoting economic stability is a major goal of economic policy and allows other macroeconomic goals to be achieved.The novelty of this study is to analyze shocks to macroeconomic variables consisting of economic growth, fiscal indicators, monetary indicators and welfare indicators by using the Panel Vector Autoregression (PVAR).The results of the study conclude that there is a causal relationship between unemployment and export; unemployment and poverty; poverty and export; unemployment and poverty.Furthermore, variables that have a one-way relationship such as economic growth affect consumption; consumption affects government spending; government spending affects investment; investment affects export.The recommendations from this study require that the government must be proactive in encouraging other elements, such as the private sector, which has a big role in helping government programs run optimally.The limitation of this research is the research methodology because all the research variables are endogenous and only analyze balance in the long run.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".