MUSE: The Bank of Canada's New Projection Model of the U.S. Economy
Bibliographic record
Abstract
The analysis and forecasting of developments in the U.S. economy have always played a critical role in the formulation of Canadian economic and financial policy. Thus, the Bank places considerable importance on generating internal forecasts of U.S. economic activity as an input to the Canadian projection. Over the past year, Bank staff have been using a new macroeconometric model, MUSE (Model of the U.S. Economy). The model is a system of estimated equations that describe, in a stock-flow framework, the interactions among the principal macroeconomic variables, such as gross domestic product (GDP), inflation, interest rates, and the exchange rate. The stock-flow equilibrium is fully described in MUSE. In steady state, the model defines specific values for all stocks, including capital stock, government debt, financial wealth, and net foreign assets. In MUSE, most behavioural equations are governed by a polynomial adjustment cost (PAC) structure. This approach is widely used in the U.S. Federal Reserve Board's FRB/US model. By allowing for lags in the dynamic equations in the context of forward-looking rational expectations, the PAC approach strikes a balance between theoretical structure and forecasting accuracy. MUSE, therefore, makes an explicit distinction between dynamic movements caused by changes in expectations and those caused by adjustment costs. Moreover, GDP is decomposed into household expenditures, business investment, government spending, exports, and imports. Hence, MUSE can be used to predict the consequences of a wide variety of shocks to the U.S. economy.
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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 | 0.000 | 0.001 |
| 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 | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".