Incorporating Scenario Analysis into the Federal Reserve’s Policy Strategy and Communications
Bibliographic record
Abstract
The U.S. economy currently faces a truly extraordinary degree of uncertainty as a consequence of the COVID-19 pandemic.In these circumstances, the Federal Reserve could begin highlighting alternative scenarios to illustrate key risks to the economic outlook, and such scenarios could inform the Fed's policy strategy and public communications.In this paper, we present a set of illustrative scenarios, including a baseline scenario with a rapid but incomplete recovery this year (an upward-tilting checkmark), a benign scenario in which an effective cure or vaccine becomes available and facilitates a nearly complete recovery by mid-2021, and a severely adverse scenario involving persistently high unemployment and disinflationary pressures.Insights into these scenarios can be drawn from key historical episodes, including the Spanish flu, the Great Depression, the end of World War II, and the global financial crisis.We conclude by identifying key challenges that the Federal Reserve might face in adjusting its monetary policy and emergency credit facilities under each of these alternative scenarios.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".