Nominal Exchange Rate Variation and World Commodity Price Variation: The Cases of Australia, Canada, and New Zealand
Bibliographic record
Abstract
World commodity prices have moved and fluctuated over time especially, the period 2000-2009. This paper examines the impact of Australia’s, Canada’s, and New Zealand’s nominal exchange rate variation on world commodity price variations in 1980-2012 and compare the impact between 1990-1999 and 2000-2009. This study employs a dynamic ordinary least square regression (DOLS) to estimate correlation coefficients that measure the volatility effects. The results show that in long-run (1980-2012), the Australian dollar, Canadian dollar, and New Zealand dollar variation raise the world commodity price variations for hard commodity which are fuel products and metal products while nominal exchange rate variation has various effects on the world commodity price variations for soft commodity which are grain products, forestry products, and livestock products. Moreover, comparison of the period 1990-1999 and the period 2000-2009 implies that exchange rate variation raises the world commodity price variations which almost price is hard commodity. Hence, the government of these hard commodities exporting countries should become aware of exchange rate variation and look after the variation because the variation causes income of export sector. In the case of developing countries, should consider exchange rate policy that impacts export sector and the economy along with give priority to future market development to be tools for absorb exchange rate variation risks.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".