Analysis of Ghana’s Gross Domestic Product from 1960 - 2019
Bibliographic record
Abstract
The most popular metric for assessing or predicting global economic progress is the GDP. The objective of this report was to analyze the GDP of Ghana from the period 1960 – 2019. Secondary data was obtained, and trend analysis was done using the linear, quadratic, and exponential trend models to determine which model best fits the dataset. Trend analysis is a technique for examining and forecasting the movements of an item based on current and historical data. The results from the trend analysis showed that the exponential trend model had the lowest Mean Absolute Percent Error (MAPE), Mean Absolute Deviation (MAD), and Mean Square Deviation (MSD). When compared to the other models, the exponential trend model fits the dataset better, which is why it was chosen to forecast Ghana's GDP. The forecast showed that Ghana's GDP is expected to grow in the coming years to about $77 billion by 2026. Agriculture is considered as the backbone of Ghana and the country imports majority if its fertilizer from Russia. The challenges the Ghanaian economy is currently facing due to inflation, the global pandemic (COVID-19), and the Russia- Ukraine conflict could have an impact on the country’ economic growth. Moreover, a change in leadership in the coming 2024 presidential election, could also have an impact on the projection. There are both positive and negative effects due to changes in leadership on economic growth. Keywords: GDP, linear, quadratic, exponential, forecast, economic DOI: 10.7176/JESD/14-2-07 Publication date: January 31 st 2023
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".