Modeling and Forecasting Gambia’s Inflation Rates
Bibliographic record
Abstract
In this paper, we examine the most appropriate method for modeling and forecasting Gambia’s inflation rates. We investigate the statistical properties of the inflation data and specify two models namely seasonal autoregressive integrated moving average (SARIMA) and k-factor Gegenbauer Autoregressive Moving Average (k-factor GARMA). The first model seasonal ARIMA(1, 1, 1)(0, 0, 1)12 was selected using the H-K Algorithm developed by Hyndman and Khandakar (2008) and 3-factor GARM A from both the spectral density graph and further analysis of the residuals from the 3-factor Gegenbauer model. The in-sample characteristics such as the Akaike Criterion and Schwarz Criterion following estimation using the first data set show that the ARIMA(1, 1, 1)(0, 0, 1)12 outperforms the 3-factor GARM A model. However, the second data set that was preserved and used for out-of-sample forecasting suggest that the 3-factor GARM A model outperforms the seasonal ARIMA(1, 1, 1)(0, 0, 1)12 model in out-of -sample forecasting. Our results indicated that inflation in Gambia is stationary with long-memory behavior at three distinct frequencies. We also found that the k-factor GARMA outperforms the seasonal ARIMA in out-sample forecasting which may be ascribed to the forecast horizon been large and series strongly long-range dependent.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".