Monetary Aggregates and Monetary Policy in the Twenty-First Century: Discussion
Bibliographic record
Abstract
years in the discussion of possible targets for monetary policy. From the perspective of other central banks, the three conferences that the Boston Fed organized on the topic of “Controlling Monetary Aggregates ” were extremely useful. They brought together key participants in the ongoing debate on the appropriate role of monetary aggregates in the formulation and implementation of monetary policy. Frank was also interested in the potential role of credit aggregates, following Benjamin Friedman’s work on that subject. And Frank and the Boston Fed had a long-standing interest in Canadian monetary policy and economic and financial developments, which was always appreciated at the Bank of Canada. Thus, I thought that it would be interesting to present the Canadian experience with targeting as a counterpoint to William Poole’s presentation on the U.S. experience. In some ways they are similar, while in others they differ appreciably. By way of introduction, I would note that the evolution over time of the conduct of monetary policy by central banks has been a function of the interactions among the performance of the economy, developments in macroeconomic and monetary theory, and the success or failure of the prevailing policy approach in achieving the central bank’s objectives. For example, the notion of an exploitable trade-off between inflation and unemployment was discredited by economic developments in the 1970s, buttressed by the analysis of Milton Friedman, Edmund Phelps, and others in the late 1960s. And the same experience of high rates of inflation
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".