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Record W2001318197 · doi:10.5539/ijef.v7n4p66

Rules vs. Discretion in Monetary Policy: From Commodity Money to Unconventional Monetary Policies

2015· article· en· W2001318197 on OpenAlexvenueno aff
Bogdan Bădescu

Bibliographic record

VenueInternational Journal of Economics and Finance · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
FundersEuropean Social Fund
KeywordsDiscretionMonetary policyEconomicsTransparency (behavior)Monetary economicsPoint (geometry)CommodityInterest ratePrice of stabilityCorrectnessMacroeconomicsFinanceComputer scienceLaw

Abstract

fetched live from OpenAlex

This paper proposes a review of the rules vs. discretion in monetary policy debate. Research shows that monetary regimes based on one or more rules had drawbacks, mainly due to the inflexibility imposed by these rules. Therefore, guiding monetary policy strictly by a set of rules is not the optimal approach. The late twentieth and early twenty-first centuries were characterized by a relaxation of the regulations in force, which led in 2008 to one of the worst economic crisis. Since the efficiency and correctness of the measures taken by the authorities as a response to the crisis are questionable and also that their long-term effects (especially the redistribution of wealth) and how to exit from them are still unknown, it becomes clear that a purely discretionary approach is not desirable either. Consequently, it should be implemented a more restrictive approach in terms of the discretionary component embedded in the authorities' decision. A starting point might be to increase the transparency regarding, for example, the algorithm by which the central banks set the policy rate or decide to increase the monetary base. In this sense, for central banks managing reserve currencies could be established publicly announced rules that govern the issuance of money and the policy rate. Moreover, such measures must be combined with improving the regulations in force, so that the attention will be also geared towards the microeconomic level, where it is most important to maintain financial stability. Last, but not least, it is important to remember that this stability should come as a result of the improvement of the general welfare, as the architects of the old Bretton Woods system envisioned.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0010.007
Scholarly communication0.0060.007
Open science0.0010.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0020.001

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 head0.031
GPT teacher head0.251
Teacher spread0.220 · 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 designTheoretical or conceptual
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
Published2015
Admission routes1
Has abstractyes

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