Automatic and Open-Source Model with Forecasts for Climate Policy and Economics
Bibliographic record
Abstract
Economic forecasts of the effects of climate policy are frequently based on static economic theory and are not regularly updated. Moreover, their source code is often not public, making replication and critical evaluation difficult. The predominant models used for climate policy narratives, so called Integrated Assessment Models, are rarely estimated using empirical data and are hence highly affected by the modeller’s assumptions. To improve the estimation of likely effects of climate policy, we present the “Aggregate Model”, a data-based model for dozens of countries that flexibly estimates and forecasts economic time series and allows for the simulation of different climate policy options. Data-based models need to incorporate long-term trends and account for both structural breaks and outliers that otherwise distort the model estimates and may lead to systematic forecast error. Our model uses various techniques from the time series literature, such as indicator saturation, model selection, and testing for co-integration. These techniques are automated to a high degree, simplifying the model estimation procedure. The Aggregate Model is distributed as an open-source R package, allowing for simple replication and modification by users through its modular structure.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.047 | 0.028 |
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".