Learning in the Oil Futures Markets: Evidence and Macroeconomic Implications
Bibliographic record
Abstract
Working Papers 2020-33 October 2020 Learning in the Oil Futures Markets: Evidence and Macroeconomic Implications Sylvain Leduc, Kevin Moran, Robert J. Vigfusson Using expectations embodied in oil futures prices, we examine how expectations are formed and how they affect the macroeconomic transmission of shocks. We show that an empirical framework in which investors form expectations by learning about the persistence of oil-price movements successfully replicates the fluctuations in oil-price futures since the late 1990s. We then embed this learning mechanism in a model with oil usage and storage. Estimating the model, we document that an increase in the persistence of TFP-driven fluctuations in oil demand largely account for investors' perceptions that oil-price movements became increasingly permanent during the 2000s before declining thereafter. We show that the presence of learning alters the macroeconomic impact of shocks, making the responses time-dependent and conditional on the views of economic agents about the shocks' likely persistence.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".