Effect of news and noise shocks of <scp>US</scp> monetary policy on economic fluctuations in emerging market economies
Bibliographic record
Abstract
Abstract This study investigates the effect of news and noise shocks of US monetary policy on economic fluctuations in emerging market economies. In the first part of our two‐step estimation method, the news and noise shocks of US monetary policy are estimated using dynamic structural vector autoregression identification. In the second step, the impact of the news and noise shocks on macro variables reflecting the business cycle (e.g., production, consumption, investment and trade balance) is examined using local projection. Our empirical results show no significant differences in the responses to both shocks at the early stage, when news and noise are not separable. However, when the monetary policy becomes known, emerging market economies enter a full‐scale recession with respect to a news shock of raised US interest rates. Meanwhile, emerging market economies enter an economic boom phase of the business cycle when the shock turns out to be noise. Fluctuations driven by noise are likely to incur greater costs than normal economic fluctuations because the former are out of sync with the fundamentals.
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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 | 0.002 | 0.015 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".