Global liquidity and commodity prices uncertainty using SFAVEC model
Bibliographic record
Abstract
This paper examines the impact of global liquidity on global commodity prices and asset prices in some major developing and developed economies. Specifically, the global liquidity on global commodity prices and asset prices is investigated using data from six major developing and emerging economies; Brazil, Russia, India, China, South Africa and Mexico (BRICSM) and four major developed economies; Canada, the European Union (EU), Japan and the US (G4) over the period 1999:01 to 2019:12. Chakraborty and Bordoloi (2019) report that global liquidity positively impacts commodity prices over time. A structural factor-augmented vector error correction model which allows for a partition among short-run and long-run is estimated. Again a robust evidence of global liquidity leads to significant and persistent upsurges in global commodity prices and global asset prices. The key finding is the positive innovations in BRICSM M2 that are linked with a positive effect on the commodity prices that is more than the impact of unexpected increases in G4 M2 on commodity prices. The commodity price uncertainty is attributed to commodity price volatility in developed and developing countries, with the uncertainty effect being more significant and persistent in emerging economies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".