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Record W2095668445 · doi:10.5539/ibr.v4n4p286

The Effects of The Interest Rate Volatility on Turkish Money Demand

2011· article· en· W2095668445 on OpenAlexvenueno aff
Yildiz SAILAM ÇELIKÖZ, Ünal Arslan

Bibliographic record

VenueInternational Business Research · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsVolatility (finance)EconometricsInterest rateTreasuryExchange rateMonetary economics

Abstract

fetched live from OpenAlex

This study aims to examine, especially the effects of interest rate (time deposit and treasury bills) volatilities on the demand for money in case of Turkey for 1987: 1-2007: 3 period. Quarterly data of all variables are used as the research data and Pesaran, Shin and Smith (2001) 's bound test is used as the research method. In computing interest rate volatilities, moving-sample standart deviation method which is proposed by Kenen and Rodrik (1986) and Koray and Lastrapes (1989) is used. According to the results, the long run coefficient of gross domestic product is positive and statistically significant as expected. Although, the volatility of interest rate on treasury bills is positive as expected, it is statistically insignificant. On the other side, the coefficient of volatility of interest rate on time deposit, the coefficient of inflation rate and the coefficient of exchange rate are all negative and statistically significant as expected.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.205
GPT teacher head0.317
Teacher spread0.111 · 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

Citations4
Published2011
Admission routes1
Has abstractyes

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