The Bank of Canada's Version of the Global Economy Model (BoC-GEM)
Bibliographic record
Abstract
The Bank of Canada's version of the Global Economy Model (BoC-GEM) is derived from the model created at the International Monetary Fund by Douglas Laxton (IMF) and Paolo Pesenti (Federal Reserve Bank of New York and National Bureau of Economic Research). The GEM is a dynamic stochastic general-equilibrium model based on an optimizing representative-agent framework with balanced growth, and some additional features to help mimic the overlappinggenerations' class of models. Moreover, there is a concrete role for fiscal policy (albeit not fully optimized) and monetary policy. At the Bank, the model has been extended beyond the standard version with tradable and non-tradable goods sectors to include both oil and non-oil commodities. Furthermore, the oil sector is decomposed into oil for production and oil for retail consumption. The authors provide a detailed technical description of the model's structure and calibration. They also describe the model's simulation properties for Canadian and U.S. domestic shocks, and describe how the model can be used to analyze issues that currently are at the forefront for the Canadian and global economies, such as trade protectionism, global imbalances, and increasing oil prices.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".