Creating a GTAP baseline for 2014 to 2050 using shock-intensive simulations
Bibliographic record
Abstract
Shock-intensive simulations can be used to: update computable general equilibrium (CGE) databases; estimate trends in industry technologies and the preferences of households, governments and importers; and generate baselines that incorporate forecasts from organizations specializing in different aspects of economies. We demonstrate the shock-intensive methodology by applying it to the Global Trade Analysis Project (GTAP) model. We update a 2014 GTAP database to 2019 with data-driven shocks to an array of macro and energy variables and describe the simulated shifts in technologies and preferences. Then, starting from the updated database, we conduct baseline simulations for 2019 to 2030, 2030 to 2040 and 2040 to 2050 in which macro and energy variables are driven by forecasts from the International Monetary Fund (IMF), International Institute for Applied Systems Analysis (IIASA) and International Energy Agency (IEA). The simulations connect disjoint years (e.g. 2019 and 2030) and use a smooth-growth assumption for savings in each region to jump over intermediate years. Investors are given forward-looking expectations so that their simulated decisions in 2030, for example, are realistic in light of prospects for 2030 to 2040. Considerable space in the paper is devoted to explaining closure swaps for facilitating shock-intensive simulations.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".