The Bank of Canada's New Quarterly Projection Model, Part 2. A Robust Method for Simulating Forward-Looking Models
Bibliographic record
Abstract
In this report, we describe methods for solving economic models when expectations are presumed to have at least some element of consistency with the predictions of the model itself. We present analytical results that establish the convergence properties of alternative solution procedures for linear models with unique solutions. Only one method is guaranteed to converge, whereas most widely used methods, including the popular Fair-Taylor approach, do not have this property. This method, which we have implemented for simulation of the Bank of Canada's models of the Canadian economy, involves solving simultaneously the full problem, "stacked" to represent each endogenous variable at each time point with a separate equation, using a Newton algorithm. We discuss briefly the extension of our convergence results to applications with non-linear models, but the strong analytical conclusions for linear systems do not necessarily carry over to non-linear systems. We illustrate the analytical discussion and provide some evidence on comparative solution times and on the robustness of the procedures, using simulations of a simple, linear model of a hypothetical economy and of two much larger, non-linear models of the Canadian economy developed at the Bank of Canada. The examples show that the robustness of our procedure does carry over to applications with working, non-linear economic models. They also suggest that the limitations of iterative methods are of practical importance to economic modellers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".