Building a Small Macro-Model for Simulation: Some Issues
Bibliographic record
Abstract
The Research Department of the Bank of Canada has decided to construct a small annual model of the Canadian economy. This paper presents the reasons for that decision as well as the methodology of the model-builders. The actual structure of the model is not described here; instead, we discuss the relationship of the model to economic theory, the choice of an estimation strategy and the proper use of the model in simulation. We thought it useful to define our preconceptions and biases before proceeding with the construction of the model, and in that way solicit comments and advice from other economists at an early stage in our work. Thus by the very nature of the paper, the views expressed are tentative and preliminary and the text should be taken as a discussion paper rather than as a finished piece. The purpose of the model is not to provide point forecasts but rather to embody as many theoretical insights as possible, especially the longer term properties implied by theory. Keeping the model small will help in ensuring that the structure is consistent with theory and will allow application of systems estimation techniques. The result will, we hope, be a model capable of performing policy simulations over the medium to long term and of describing changes in private sector behaviour that result. It is impossible to give a brief summary of the paper here, but among the topics discussed are our views on the proper treatment of expectations, the value of the 'disequilibrium' concept and the problems of aggregation.
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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 | 0.000 | 0.000 |
| 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".