Is there an Optimal Forecast Combination? A Stochastic Dominance Approach to Forecast Combination Puzzle
Bibliographic record
Abstract
Even though different optimal forecast combination weights are offered for static, dynamic, or time-varying situations, empirical findings support the simple average forecast combination outperforms more sophisticated weighting schemes and/or the best individual model. Using an approach that relies on consistent tests for stochastic dominance efficiency, an alternative optimal weighting scheme is proposed. These tests are considered for a given forecast combina-tion (i.e. equal weighted average of forecasts) with respect to all possible forecast combinations constructed from a set of individual forecasts to obtain optimal or worst forecast combina-tions. In our empirical applications we find that equally weighted forecast combinations are neither optimal nor the worst forecast combination. For the optimal forecast combination, the best forecasting model, i.e. the model which assumes relatively more weight than other forecast models, differs with the variable being forecasted and for different forecast horizons. On the other hand, random walk is the model that consistently contributes with more than arbitrarily assigned equal weights for the worst forecast combination for all variables being forecasted and for all forecast horizons.
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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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".