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Record W4322502047 · doi:10.3390/jrfm16030152

Stylized Facts of the FOMC’s Longer-Run Forecasts

2023· article· en· W4322502047 on OpenAlexvenueno aff
Jaime Márquez

Bibliographic record

VenueJournal of risk and financial management · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsStylized factFederal fundsMonetary policyEconomicsRule of thumbFederalistQuantitative easingMonetary economicsFederal Reserve Economic DataEconometricsMacroeconomicsCentral bankPolitical science

Abstract

fetched live from OpenAlex

Conventional explanations of monetary policy decisions in the United States assume that the longer-run Federal funds rate is determined by a representative central banker (i.e., the Fed) using longer-term forecasts of economic activity and unemployment. This assumption is inconsistent with the federalist structure of the Federal Reserve in which the Federal funds rate is determined by a committee made up of the Federal Reserve Board and the Federal Reserve Banks. This inconsistency would be irrelevant if differences in the Fed participants’ longer-run projections were small or constant, but they are not: disparities in these longer-run projections are large and volatile. This finding raises several questions: Are FOMC participants relying on the same forecasting framework (i.e., model or rules of thumb) but using different values for the forecast drivers? Or are these participants using the same forecast drivers but relying on different frameworks?

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.040
GPT teacher head0.212
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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