Applying the ARIMA Model to the Process of Forecasting GDP and CPI in the Jordanian Economy
Bibliographic record
Abstract
Gross Domestic Product (GDP) and consumer price index (CPI) are significant indicators to describe and evaluate economic activity and levels of development. They are also often used by decision makers so as to plan economic policy. This paper aims at modeling and predicting GDP and CPI in Jordan. In order to achieve this goal, the study applied the Box- Jenkins (JB) methodology for the period 1976-2019. Based on the results, ARIMA (3,1,1) found to be the best model for the GDP. In addition, ARIMA (1,1,0) was the best model for forecasting the CPI. The results were supported with the findings of the stationarity and identification rules test of time series under using AIC and SIC criterion. The forecasted values of the GDP and the CPI for the next three years (2020-2022) were (29342.12, 32095.10, 35106.36 million JD) and (128.31, 133.28, 139.28) respectively. Compared with 2019, the GDP is forecasted to decrease in 2020, while the CPI is forecasted to increase in 2020. This implies that the Jordanian economy is tending toward stagflation. After 2020, both GDP and CPI increased, which indicates that Jordanian economy is tending toward cost-push inflation.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".