Impact of monetary and fiscal policy to Bursa Malaysia Plantation Index / Yaqin Yunus and Amalina Ismail
Bibliographic record
Abstract
Play a role to regulate monetary flow while fiscal policy imposed by Malaysian Government play a role to manage public monetary. Progressive growth and development of country economy can be achieved through effective monetary and fiscal policy. Plantation is one of important industry in Malaysia that contribute to the country economy and social interest. In 2016r agriculture sector stood at 8.1 per cent or RM89.5 billion to the Gross Domestic Product (GDP). The purpose of this study is to determine the relationship of monetary and fiscal policy with plantation economy in Malaysia. Overnight Policy Rate (OPR) is used as monetary policy instrument and fiscal expenditure and government revenue are used as fiscal policy instruments while Bursa Malaysia Plantation Index is used as plantation sector economic performance. This study used 30 samples of secondary data from 1st quarter 2011 until 2nd quarter of 2018. The research is examined with unit root test, cointegration test. VECM Model and Granger Causality Test and diagnostic test using quarter data from year 2011 until 2018. The results revealed that fiscal expenditure and government revenue have significant impact to Bursa Malaysia Plantation Index in long run while Overnight Policy Rate (OPR) has significant impact to Bursa Malaysia Index in short run. Thus, monetary policy and fiscal policy should also be considered among all factors that impact the plantation economy. Policy makers, investors and corporations may utilize these findings for their strategic decision.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".