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Record W2559123199 · doi:10.15405/epsbs.2016.11.02.12

Household Debt and Macroeconomic Variables in Malaysia

2016· article· en· W2559123199 on OpenAlexaboutno aff
Masturah Ma’in, Nur AmiraIsmarau Tajuddin, Siti Badariah Saiful Nathan

Bibliographic record

Venue˜The œEuropean Proceedings of Social & Behavioural Sciences · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsDebtGross domestic productUnemploymentIndex (typography)Household debtQuarter (Canadian coin)Ordinary least squaresExternal debtPrice indexVariablesInterest rateDebt-to-GDP ratioMonetary economicsMacroeconomicsEconometricsGeographyMathematics

Abstract

fetched live from OpenAlex

The rise of household debt in Malaysia has caused consternation since it has almost reached 89.1% of total GDP. The level of household debt is deemed to be at worrying stage as it may trigger another financial crisis. The purpose of this study is to examine factors that influence household debt in Malaysia via time series data. This study employs the ordinary least square (OLS) method and the macroeconomic variables used consist of base lending rate, housing price index, gross domestic product and unemployment as independent variables taken in the period from quarter one 2008 to quarter four 2015. The results show that the housing price index is the most significant variable, followed by base lending rate, unemployment and gross domestic product. House pricing index and gross domestic product show positive relationships with household debt, which indicates that the rise of household debt is determined by the rise of these explanatory variables. However, base lending rate and unemployment are found to have negative effects on the rise of household debt. The data are taken from Bank Negara Malaysia report, National Property Information Centre (NAPIC) and Asia Regional Integration Centre.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.357

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.022
GPT teacher head0.217
Teacher spread0.195 · 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.

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

Citations12
Published2016
Admission routes1
Has abstractyes

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Same venue˜The œEuropean Proceedings of Social & Behavioural SciencesSame topicIslamic Finance and Banking StudiesFrench-language works237,207