DRAFT, PRELIMINARY, COMMENTS ARE APPRECIATED. Simple Rules in the M1-VECM *
Bibliographic record
Abstract
This paper analyses various simple interest rate rules using a vector error correction forecasting model of the Canadian economy that is anchored by long-run equilibrium relationships suggested by economic theory. Dynamic and stochastic simulations are performed using several interest rate rules, including money based rules and their properties are analysed. Among the class of rules we consider in this model, we find that a simple rule with interest rate smoothing minimizes the volatility of output, inflation and interest rate. This rule dominates Taylor-type, Ball and other simple rules. * FR-01-002.We would like to thank Scott Hendry, Dinah Maclean, Pierre St-Amant for helpful suggestions and discussions. Thank you also to Sharon Kozicki our discussant at the CEA 2001 meetings in Montreal, Chris Graham for providing technical help, Jim Day for providing help with the graphs and participants at the brown bag meeting. The views in this paper are those of the authors and should not be attributed to the Research on monetary policy rules has exploded in the last few years. Much of this research has focused on finding a simple benchmark rule that the central bank can use in its decision
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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 | 0.005 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.306 | 0.098 |
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".