SHORT-RUN AND LONG-RUN RELATIONSHIP OF ISLAMIC HOME FINANCING IN MALAYSIA: ANALYSIS ON ISLAMIC BANKS
Bibliographic record
Abstract
Islamic home financing is a service offered by Islamic banks according to the Islamic principle. In Malaysia, it has been offered since the early 1980s. The demand for Islamic home financing in Malaysia has increased over time. Therefore, this study would like to delve into the short-run and long-run relationship between the factors involved in Islamic home financing. Past studies highlighted the microeconomic factors and macroeconomic factors in Islamic banks that contribute to the growth of Islamic home financing in Malaysia. Thus, this study would include four factors comprised of deposits, liabilities, GDP, and government expenditure. Deposits and liabilities are categorized into microeconomic factors, whereas GDP and government expenditure are macroeconomic factors. The first objective is to identify the short-run and long-run relationship between the factors mentioned above on Islamic home financing in Malaysia. The second objective is to analyze the causality effects of the factors on Islamic home financing in Malaysia. The methodology comprises of quantitative research design. Data collection is based on secondary data collection, which was retrieved through documentation review and statistical highlights from Bank Negara Malaysia. The data comprise the first quarter of 2010 until the second quarter of 2021. Analysis data is conducted through an econometric approach within time series data which is the ARDL test and Granger causality test. The findings of this study would emphasize the importance of microeconomic factors and macroeconomic factors on Islamic home financing. It would encourage the supply and growth of Islamic home financing by Islamic banks in Malaysia in the future.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".