DEEP DIVE INTO COMMODITY MARKETS: EXPLORING THE COMMODITY–INDUSTRIAL PRODUCTION NEXUS
Bibliographic record
Abstract
This article explores the commodity–industrial production nexus. More precisely, we assess the cointegration relationships between commodity markets and industrial production during 1993-2011, with an overview for several countries: the USA, the EU, Australia, China, Brazil, Canada, Germany. First, we explore the descriptive statistics and unit root tests of the dataset. Second, we develop two kinds of cointegration analyses (e.g. with/without structural break) between commodities on the one hand, and industrial production indices on the other hand. Third, we conclude on the main results achieved by this econometric procedure. The key contribution of our paper is to revisit the link between industrial production and commodity prices, by using an econometric methodology incorporating structural breaks, and by using a very recently updated dataset. By carrying out a systematic comparison between our results and papers previously published in this literature, we gain a wealth of insights.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".