The Bank of Canada's New Quarterly Projection Model, Part 1. The Steady-State Model: SSQPM
Bibliographic record
Abstract
This report is the first documenting the Bank of Canada's new model of the Canadian economy, the Quarterly Projection Model (QPM). QPM is used at the Bank of Canada for both economic projections and policy analysis. Here the authors focus on the model's long-run properties, describing SSQPM, a model of the steady state of QPM that is maintained separately and used to study the determinants of long-run equilibrium in the economy and the permanent effects of economic disturbances or changes of policy. SSQPM is based on the simple Blanchard-Weil model with overlapping generations. In such a model, household preferences determine the steady-state level of financial wealth, relative to output. The equilibrium is achieved primarily through variation in the level of net foreign assets. This then determines foreign debt service and the capital account of the balance of payments. Given these "asset" considerations, the current account identities provide the required trade balance, and in SSQPM, the real exchange rate adjusts to ensure that this level of trade is achieved. The authors present the simplest form of such a model and then introduce a series of elaborations and extensions that are judged necessary to support a working projection environment. They then describe the choices made by Bank staff in calibrating the model and the numerical steady state that emerges. Finally, the authors describe the properties of SSQPM, as revealed by its responses to a number of shocks to exogenous variables.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".