The Performance and Robustness of Simple Monetary Policy Rules in Models of the Canadian Economy
Bibliographic record
Abstract
In this report, we evaluate several simple monetary policy rules in twelve private and public sector models of the Canadian economy. Our results indicate that none of the simple policy rules we examined is robust to model uncertainty, in that no single rule performs well in all models. In fact, our results show that the performance of some of the simple rules, particularly interest-rate-smoothing rules and rules that have a high coefficient on the inflation gap, can substantially deviate from the optimal rule and can even be unstable in some models. Our results are thus very different from those of Levin, Wieland, and Williams (1999), who argue that simple policy rules are not only robust but also generate essentially the same policy frontier as more complicated rules or rules that respond to a large number of variables. Furthermore, we find that open-economy rules do not perform well in many models. In fact, we find that adding an exchange rate term to a simple policy rule often increases the loss-function value. This result is thus very different from that of Ball (1999), who argues in favour of a rule that includes the exchange rate. Although it is not robust, we find that a simple nominal Taylor-type rule that has a coefficient of 2 on the inflation gap and 0.5 on the output gap outperforms the other simple rules in a certain class of models. But even in those models the loss-function value of this simple rule can substantially deviate from the optimal or base-case rule.
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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 | 0.001 | 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".