Interpreting a Monetary Conditions Index in economic policy
Bibliographic record
Abstract
The main purpose of this paper is to review and interpret the use of a Monetary Conditions Index (or MCI) by central banks in the conduct of monetary policy. Numerous central banks, governmental organizations, and businesses now calculate an MCI as an indicator of the stance of monetary policy. Two central banks, those for Canada and New Zealand, use their MCIs as operational targets. This paper describes and defines the concept of an MCI, summarizes how central banks implement MCIs in practice, reviews some of the operational and conceptual issues involved, and evaluates the sensitivity of MCIs to an inherent source of uncertainty in their calculation. Empirically, this uncertainty typically results in MCIs that are uninformative as indicators of monetary conditions, so some possible alternatives are briefly considered. 1 A Monetary Conditions Index in practice Several central banks calculate a Monetary Conditions Index for use in monetary policy. Empirically, an MCI is a weighted average of changes in an interest rate and an exchange rate relative to their values in a base period. The weights on the
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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 | 0.013 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".