Laurence H Meyer: The economic outlook and the challenges facing monetary policy
Bibliographic record
Abstract
When I’m asked what my profession was prior to joining the Board of Governors, I do not say that I was an economic forecaster, but rather a storyteller. As a forecaster, I had learned that neither my students nor my clients wanted to be buried in reams of computer output. They wanted me to tell a story that brought together in a coherent way the implications of the large number of economic indicators they saw as otherwise unconnected and therefore confusing. Since joining the Board, I have tried to continue this approach, focusing in particular on the forces behind the extraordinary macroeconomic performance over the past several years and their implications for the outlook and monetary policy. My story this morning has five chapters on how the economy and monetary policy have adjusted, and must continue to adjust, to the acceleration of productivity and to the oil and other relative-price price shocks that have been so important in shaping macroeconomic performance since the mid-1990s. In Chapter 1, I identify the short-run effects of higher productivity growth- specifically, the effects on aggregate demand and on inflation- and assess the implications of these two effects for the conduct of monetary policy. In Chapter 2, I note the favorable choice that confronts policymakers as the economy adjusts to an
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".