Business Cycle Asymmetries and Forecast Performance of Neural Networks Models in 'G5' Countries' Growth Rates
Bibliographic record
Abstract
In this study, we compare forecast performance of neural network models with linear models in real GDP growth rates for Canada, France, Japan, United Kingdom (UK) and the United States of America (USA). We demonstrated in our previous work (Kiani and Bidarkota (2002) and Kiani (2003a & 2003b)) that performance of neural network models is superior to both linear and nonlinear models. However, in this study we compare in-sample as well as out-of-sample performance of neural network models with linear models in all series. Based on our study results, we conclude that networks models were more accurate than linear models for in-sample forecasts. However, when comparing the out-of-sample forecast performances, linear models performed better than the neural networks models in all series. Our results suggest that empirical researchers should not blindly trust the results of the traditional statistical tests that are in-sample based.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".