The ENSO Effect on World Wheat Market Dynamics: Smooth Transitions in Asymmetric Price Transmission
Bibliographic record
Abstract
Climate anomalies, such as El Nino Southern Oscillation (ENSO), affect agricultural production in different parts of the world, and can impact price behavior of the internationally traded commodities. This study examines the effect of ENSO on wheat price dynamics of five major exporting regions -- USA, Canada, Australia, EU, and Argentina. While the prices are linked due to the law of one price and the arbitrage conditions, nonlinear price adjustments are expected, due to the transaction costs, the market power, derived asymmetries from supply shocks. This study addresses asymmetries in wheat price transmission in response to ENSO-related supply shocks, using univariate and multivariate smooth transition modelling frameworks. Results of this study confirm regime-dependent nonlinearities in ENSO cycles as well as the system of considered wheat prices, where regimes are conditioned on the state of nature of the ENSO anomaly. In general, positive ENSO shocks, i.e. El Nino-s, result in wheat price reduction, while negative ENSO shock, i.e. La Nina-s, results in increased wheat prices. Moreover, the asymmetric nature of the responses to ENSO shocks implies that the rates of price increases are, on average, larger as compared to the rates of price decreases.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".