Identifying oil price shocks with global, developed, and emerging latent real economy activity factors
Bibliographic record
Abstract
Summary This paper proposes an identification strategy for international oil price shocks while accounting for the heterogeneous sources of oil demand from global, developed, and emerging economies. Unlike existing works, we isolate global oil demand shocks, associated with a global real economic activity factor, from oil demand shocks originating specifically from developed and emerging economies, associated with real economic activity factors within these two groups of economies. The paper uses a structural factor‐augmented vector autoregression (FAVAR) model with latent global and specific factors to model crude oil demand and supply. To identify the shocks, we extract real economic activity factors from a large panel of emerging and developed economies' real activity variables using a two‐level factor model. The paper shows how structural shocks can be identified by solving equations that arise from economically meaningful zero restrictions on the impact matrix of the reduced‐form FAVAR model innovations. The empirical application shows that identifying the international oil demand shocks based on the global and specific latent factors is essential to appropriately quantify their heterogeneous impacts on these factors, the crude oil production, and the real oil price.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".