Macroeconomic Instability Index and Malaysia Economic Performance
Bibliographic record
Abstract
The economic performance of Malaysia was affected by a series of financial crises that had induced macroeconomic instability in the country, which in turn had immensely dampened the nation’s economic growth rate. No doubt Malaysia needs an indicator to monitor the nation’s economic performance from time to time. This study attempts to construct such indicator known as Macroeconomic Instability Index (MII). The constructed MII shows two significant spikes at 1998 and 2008, which correspond to the Asian Financial Crisis and US Subprime Mortgage respectively, that had resulted in negative growth rate for GDP of Malaysia in 1999 and 2010. Results obtained from further analysis by the ARDL technique show that MII has negative and significance effects on economic performance. Moreover, MII has predictive power against economic performance as early as two periods in advance. The constructed MII could serve as end-product for policy purposes or intermediate-product for other economic and finance studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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; both teacher heads agree on what is shown here.
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".