Oil, the Baltic Dry index, market (il)liquidity and business cycles: evidence from net oil-exporting/oil-importing countries
Bibliographic record
Abstract
Abstract The recent financial crisis has made (il)liquidity research more significant than ever. Galariotis and Giouvris (Int Rev Financ Anal 38:44–69, 2015) find evidence that market liquidity may contain information for predicting the state of the economy. Similar to (il)liquidity, oil is an important indicator of the future state of the economy (GDP). We consider five predictive variables, namely national/global illiquidity, foreign exchange, Baltic Dry, and oil. Our findings show that (1) global illiquidity provides greater overall explanatory power compared to national illiquidity (even for developed oil exporters: Norway, Canada, and Denmark). (2) Oil is the most important predictive variable for oil exporters (especially for emerging oil exporters suggesting over-reliance), while Baltic Dry appears to be more important for oil importers. (3) FX has extra power over financial variables mainly for emerging oil exporters. Finally, there is a two-way causality between GDP and our predictive variables: (4) For oil exporters, the two-way causality between oil and GDP remains, while for net oil importers, we observe a one-way causality from GDP to oil.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".