Bibliographic record
Abstract
The study investigated the empirical role of past values of G 7 countries GDP growth rates in its future realizations. Using the Box–Jenkins modelling method, the study utilized 250 in-sample quarterly time series data to forecast out-of-the-sample G7 countries GDP growth rates. The study sourced the GDP growth data from World Bank World Development Indicators (WDI) for the period between 2002 to 2022. The study results predict that G 7countries GDP will, on average, experience 4 percent quarterly growth rates for the coming three and half years. To solidify the validity of the forecasting results, the study conducted several ARIMA and rolling window diagnostic tests. The model errors proved to be white noise, the moving average (MA) and Autoregressive (AR) components are covariances stationary, and the rolling window test shows model stability within a 95% confidence interval. Purpose: To explore the effectiveness of ARIMA model in forecasting GDP of G 7 countries. Findings: The results indicate that the GDP data of the G7 countries (Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States), the ARIMA (Autoregressive Integrated Moving Average) model proved effective in capturing the historical trends of GDP fluctuations. Practical Implications: These ARIMA-based forecasts can serve as useful tools for policymakers, allowing them to anticipate potential economic downturns and formulate appropriate monetary or fiscal policies. Financial institutions can integrate these GDP forecasts into their risk models to better manage credit risk, especially in countries with volatile GDP trends like the UK and Italy. Originality/value: The study provides a novel comparative perspective on GDP forecasting across multiple advanced economies, using ARIMA to identify country-specific economic trends. This multi-country analysis adds value to existing literature, which often focuses on individual countries.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".