Uncertainty and Monetary Policy Experimentation: Empirical Challenges and Insights from Academic Literature
Bibliographic record
Abstract
Central banks face considerable uncertainty when conducting monetary policy. Some of the reasons for this include limitations of economic data, the unobservability of key macroeconomic variables such as potential output, structural changes to the economy and disagreements over the correct model for the transmission of monetary policy. At the same time, monetary policy is affected by uncertainty from various sources, including lack of or imperfect observation of economic variables, structural economic changes and possible misspecifications using models. We draw from the academic literature to review some of the key sources of this uncertainty and their implications for the conduct of monetary policy. First, we discuss evidence on release lags and revisions to economic data. We also highlight uncertainty around measuring unobservable variables such as the output gap and the natural rate of unemployment. The strength of a trade-off between these measures of economic slack and inflation—a cornerstone of monetary policy—is itself subject to continuous reassessment. Second, the literature finds that different sources of uncertainty may make the optimal conduct of monetary policy either more or less responsive to economic shocks. Additionally, the benefits of tackling uncertainty by engaging in purposeful monetary policy experimentation are typically small but may become more significant during major structural change or following unprecedented shocks.
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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 | 0.072 | 0.320 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".