A study on determinants of household debt in Malaysia / Fatin Nurhaziqah Mat Isa
Bibliographic record
Abstract
Household debt in Malaysia has always being the popular issue as it keep raising and will give impact towards the stability of economic growth. This study is conducted to determine the relationship between independent variables and dependent variables which independent variables consist of Gross Domestic Product (GDP), Housing Price Index (HPI), Interest Rate (IR), Unemployment Rate (UR) and Inflation Rate (IFR). This study is using time series analysis which data collected from period of Quarter One 2000 until Quarter Four 2018 and covers for Malaysia only. The quarterly time series data were obtained from Thomson Reuters Data Stream, Bank Negara Malaysia and World Bank Data. In order to obtain the empirical result, Multiple Linear Regression model is applied to obtain the relationship between independent and dependent variables. By using Multiple Linear Regression model, the result concludes that Housing Price Index (HPI), Interest Rate (IR) and Unemployment Rate (UR) has statistically significant impact towards the level of household debt in Malaysia with positive correlation except for Interest Rate (IR) with negative relationship. However, Gross Domestic Product (GDP) and Inflation Rate (IFR) were found to have insignificant relationship between household debts with negative correlations. Based on the results obtained, recommendations are made for the significant of study to help them in improving the household debt level in the long run.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".