Bibliographic record
Abstract
What drives commodity price booms and busts?We provide evidence on the dynamic effects of commodity demand shocks, commodity supply shocks, and inventory demand shocks on real commodity prices.In particular, we analyze a new data set of price and production levels for 12 agricultural, metal, and soft commodities from 1870 to 2013.We identify differences in the type of shock driving prices of the various types of commodities and relate these differences to commodity types which reflect differences in long-run elasticities of supply and demand.Our results show that demand shocks strongly dominate supply shocks.* The views in this paper are those of the authors and do not necessarily reflect the views of the Federal Reserve Bank of Dallas or the Federal Reserve System.Jacks gratefully acknowledges the Social Science and Humanities Research Council of Canada for research support.We are grateful for comments and suggestions from participants at the Summer Meeting of the Association of Environmental and Resource Economists, at the Bank of Canada and Federal Reserve Bank of Dallas joint conference on commodity price cycles, at the Norges Bank/CAMP workshop, and at seminars at the Bundesbank and the European Central Bank.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".