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Record W7154657746

The Relationship And Causal Effects Of Determinant Factors Of Islamic Home Financing In Malaysia

2024· other· en· W7154657746 on OpenAlexaboutno aff
Siti Maryam Idris

Bibliographic record

VenueUniversiti Sains Malaysia Institutional Repository (Universiti Sains Malaysia) · 2024
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsIslamCointegrationDistributed lagQuarter (Canadian coin)Granger causalityCausality (physics)Statistical analysisIslamic finance
DOInot available

Abstract

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Based on the period of establishment, conventional banks were established long before islamic banks in malaysia. Many aspects can contribute to the stability in conventional banks. However, in islam, the criteria offered by conventional banks are opposed from shariah principle. Therefore, islamic banks were established to promote services in banking according to shariah principle. In accordance with this matter, this study would like to assess the relationship and causal effects of factors determinant in islamic home financing service. The factors are categorized into internal factors and macroeconomic factors. The first objective is to examine the long-run relationship of determinant factors in islamic home financing in malaysia. Follows by second objective, to examine the short-run relationship of determinant factors in islamic home financing in malaysia. Third objective is to assess the causality effect of determinant factors in islamic home financing in malaysia. The methodology of this study is quantitative research design. The data collection is based on secondary data which is retrieved through documentations review and statistical highlights from bank negara malaysia. The statistical data comprise of quarterly data from the first quarter of 2007 until the fourth quarter of 2021. Analysis data is conducted by using econometric approach on time series data which is autoregressive distributed lag (ardl) cointegration test and granger causality test.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.717
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.002
Science and technology studies0.0010.003
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.213
Teacher spread0.203 · 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 teacher head, not a consensus.

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
Published2024
Admission routes1
Has abstractyes

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