Nexus between Macroeconomic Factors and Economic Growth in Malaysia: An Autoregressive Distributed Lag Approach
Bibliographic record
Abstract
This study digs into the complex relationship that exists between the development of GDP in Malaysia and major macroeconomic variables. It is of the utmost importance to gain an understanding of the elements that influence GDP development to reduce the risk of sociopolitical instability. Because nations are becoming more aware of the various elements that could potentially affect economic growth, this study was prompted to determine the precise mechanisms that are at play because of this awareness. This study employs the Autoregressive Distributed Lag (ARDL) methodology to yield robust statistical insights into the nexus between macroeconomic variables and economic growth in Malaysia. We have used quarterly data ranging from the initial quarter (Q1) of 2000 to the last quarter (Q4) of 2020 for our analysis. The findings of this study provide important insights into the dynamic links between GDP growth and the selected macroeconomic determinants. As a result, the findings provide policymakers, academics, and practitioners with significant information that can be used to design economic plans that are informed by relevant data. In addition, this study emphasizes the necessity for future research endeavors to go deeper into this topic, bringing attention to the requirement for new views and the active participation of new academics, politicians, and practitioners. This concerted effort is necessary to promote sustainable economic growth and stability in Malaysia and elsewhere.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".