MétaCan
Menu
Back to cohort
Record W4376613158 · doi:10.32782/infrastruct68-42

ANALYSIS OF THE APPLICATION BY THE NATIONAL BANK OF UKRAINE OF THE MAIN TOOLS AND LEVERS OF MONETARY AND CREDIT POLICY

2022· article· en· W4376613158 on OpenAlexaboutno aff
Liubov Petik, Alina Mlintsova

Bibliographic record

VenueMarket Infrastructure · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Issues in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsNational bankLegislationMonetary policyInterest rateEconomicsLoanBondBusinessOpen market operationMonetary economicsExchange rateFinancial systemMacroeconomicsFinance

Abstract

fetched live from OpenAlex

The purpose of scientific research is determined, namely analysis application of the main instruments by the National Bank of Ukraine and levers of influence to ensure the optimal level of monetary and credit politicians. The list of key levers of influence of the National Bank of Ukraine on the monetary sector is considered. The economic essence of the concept of "instruments of monetary policy" is highlighted. The key instruments of direct and indirect influence of monetary policy are defined. The limits of influence of instruments of monetary activity are characterized. The essence of the concept of "accounting rate of the National Bank of Ukraine" within the limits of the current legislation is given. The dynamics of the discount rate of the National Bank of Ukraine for the years 2017-06/03/2022 were considered. The role of the influence of the National Bank of Ukraine on the discount rate is determined. It is noted that the discount rate affects the volumes of household consumption and investment, as well as inflation. According to the current legislation, it is determined that the National Bank for the purpose of regulation of the money market can carry out transactions with purchase/sale of government bonds of Ukraine both on the stock exchange and over-the-counter stock market. The essence of the concept of "domestic state loan bonds" within the limits of the current legislation is highlighted. An analysis of the volume and number of domestic state loan bonds placed on the primary market in terms of currencies during 2015–2021 was carried out. The purpose of operations on the secondary market by the National Bank of Ukraine has been determined. The dynamics of the volume of executed agreements on the purchase/sale of domestic government loan bonds on the secondary market during the analyzed period were studied. The indicators of the money supply in terms of monetary aggregates are presented and characterized. The volume of operations on the mobilization of banks' funds by placing deposit certificates for the years 2015–2021 was analyzed. It is noted that these indicators affect business activity in the country, pricing, investment level, money supply, etc. The use of monetary policy instruments by such foreign countries as Canada, Italy, Japan, the USA and Germany is characterized.

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)0.002
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0000.002
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 head0.009
GPT teacher head0.206
Teacher spread0.197 · 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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueMarket InfrastructureSame topicEconomic Issues in UkraineFrench-language works237,207