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Record W4393442642 · doi:10.33423/jabe.v26i1.6887

Effect of Federal Funds Rate on CPI and PPI

2024· article· en· W4393442642 on OpenAlexvenueno aff
Deergha Raj Adhikari, David Stevens

Bibliographic record

VenueJournal of Applied Business and Economics · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsFederal fundsBusinessMonetary economicsEconomicsMonetary policy

Abstract

fetched live from OpenAlex

One of the crucial jobs of central banks is to rein in inflation as it creates uncertainty in the economy and in private investment and ultimately negatively impacts the economy. If the source of inflation is positive demand shock, then raising the federal funds rate target is the right way to rein in inflation. If the source of inflation is negative supply shock, then raising the federal funds rate target will make things worse. In this study, the impact of FFR (federal funds rate) on CPI (consumer price index) and producer price index (PPI) is examined. Findings indicate that raising the federal funds rate will have a negative impact on both CPI and PPI with a 2-period lag. The possible explanation of this finding is that raising federal funds rate lowers aggregate demand, lowers the price level and thereby the CPI. And when CPI falls, it lowers per-unit profit, prompting producers to cut supply, which in turn lowers the demand for producer goods and services, and thereby lowers PPI.

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)0.023
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.025
GPT teacher head0.213
Teacher spread0.187 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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