Large-Scale Asset Purchases: Impact on Commodity Prices and International Spillover Effects
Bibliographic record
Abstract
Prices of commodities, including metals, energy and agricultural products, rose markedly over the 2009–2010 period. Some observers have attributed a significant part of this increase in commodity prices to the U.S. Federal Reserve’s large-scale asset purchase (LSAP) programs. Using event-study methodologies, this paper investigates whether the announcement and subsequent implementation of the Fed’s LSAPs, and communication of the tapering of these purchases, affected commodity prices. Our empirical results suggest that LSAP announcements did not lead to higher commodity prices. However, there is some evidence that the currencies of commodity exporters appreciated and that their stock markets posted gains. The results suggest that other factors, such as supply constraints and robust demand from emerging-market economies, were the likely drivers behind the increase in commodity prices. Last, the paper finds that commodity prices have become more sensitive to macroeconomic news when monetary policy is at the effective lower bound.
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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) | 0.008 | 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".