Disaggregation and Forecasting of the Monthly Indonesian Gross Domestic Product (GDP)
Bibliographic record
Abstract
Gross Domestic Product (GDP) is considered as the best measure of economicperformance. However, in Indonesia, the GDP is presented in quarterly aggregate value.As a result, the monthly economic outlook is unknown, and analysis with other monthlyeconomic variables becomes limited. Therefore, this study will disaggregate quarterlyGDP into monthly GDP and its forecasting by using one of the coincident indicatorswhich are monthly Production Index of Large and Medium Manufacturing (industrialproduction index). Disaggregation is done on National GDP data of Indonesia period2000/I to 2016 / IV, whereas forecasting is made on monthly and quarterly GDP 2017.This study uses a combination of the simple linear regression model and ARIMA modelwith some modifications. The disaggregation result indicates that the monthly GDPmoves volatile and has a different pattern between quarters. Also, the monthly GDPdisaggregation and forecasting are proven that can be used by industrial productionindex that becomes a coincident indicator. GDP 2017 shows that the highest quarterlyGDP will have occurred in the third quarter, whereas the highest monthly GDP willhave occurred in June (second quarter). The result of disaggregation can be used furtherto the study of economic outlook will be more comprehensive.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".