Study of Economic Growth in IKN based on Autoregressive and Distributed Lag Approach
Bibliographic record
Abstract
Indonesia's economy plays an important role in supporting national development and government policies in various sectors such as education, health, and infrastructure. In the first quarter of 2024, Indonesia's economy experienced an increase from the same period in 2022. East Kalimantan experienced significant growth supported by the mining sector, metal industry, and the National Capital City project. However, East Kalimantan is dependent on raw material exports and faces challenges in economic transformation. The government aims to increase exports of processed products to reduce poverty and unemployment. This study analyzes whether economic growth in IKN affects the economy of East Kalimantan, by considering inflation, CPI, export value, and GRDP. This study uses quantitative research methods using Autoregressive Distributed Lag (ARDL) with the advantage that it can be used in models with different levels of stationary and does not matter the number of samples with the data used is secondary data from BPS. The best model obtained is ARDL (3, 3, 4, 3, 4) based on the smallest AIC value which shows the long-term and short-term relationship. Economic growth, export value, and GRDP from the previous quarter affect growth negatively, while GRDP from the same period and the previous quarter affect growth positively. In the long run, export value and GDP significantly affect growth. These results provide insights for the government in managing East Kalimantan's growth, supporting sustainable development and SDG achievement. The results of this study are expected to be a reference for the central government to make policies related to factors that affect Economic Growth in the hope of increasing economic growth in East Kalimantan.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".