Monetary Policy Under Uncertainty: Practice Versus Theory
Bibliographic record
Abstract
For central banks, conducting policy in an environment of uncertainty is a daily fact of life. This uncertainty can take many forms, ranging from incomplete knowledge of the correct economic model and data to future economic and geopolitical events whose precise magnitudes and effects cannot be known with certainty. The objective of this paper is to summarize and compare the main results that have emerged in the literature on optimal monetary policy under uncertainty with actual central bank behaviour. To this end, three examples are studied in which uncertainty played a significant role in the Bank of Canada’s policy decision, to see how closely they align with the predictions from the literature. Three principles emerge from this analysis. First, some circumstances—such as when the policy rate is at risk of being constrained by the effective lower bound—should lead the central bank to be more pre-emptive in moving interest rates, whereas others can rationalize more of a wait-and-see approach. In the latter case, the key challenge is finding the right balance between waiting for additional information and not falling behind the curve. Second, the starting-point level of inflation can matter for how accommodative or restrictive policy is relative to the same situation without uncertainty, if there are thresholds in the central bank’s preferences associated with specific ranges for the target variable, such as the risk of inflation falling outside of the inflation control range. Third, policy decisions should be disciplined, where possible, by formal modelling and simulation exercises in order to support robustness and consistency in decision making over time. The paper concludes with a set of suggested areas for future research.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".