The changing international transmission of US monetary policy shocks: is there evidence of contagion effect on OECD countries
Bibliographic record
Abstract
We study the changing international transmission of US monetary policy shocks to 14 OECD countries over the period 1981-2010. We use a Time Varying Parameter Factor Augmented VAR approach (TVP-FAVAR) to study the EFFR shocks together with a large data set of 265, major financial, macroeconomic and trade variables. Our main findings are as follows: First, negative US monetary policy shocks have considerable negative impact on GDP growth in US, Canada, Japan and Sweden while most of the other member countries benefits (with France being most benefited). Second, the transmission to GDP growth has increased in OECD countries since the early 1980s. We also detect a more depressed GDP over medium term in US, Canada, Japan, Australia, Norway and Sweden over the recent Global Financial Crisis. Third, the size of US monetary policy shocks during financial turmoil periods were unusual than normal periods and varies overtime. The Financial Crisis (2008-2009) is evidenced by decline in residential investment in US and propagation of this shock to Canada, Germany, Japan, Switzerland and New Zealand over the recent period. US monetary policy shocks reduce share prices in most of the OECD countries; this impact is more pronounced over the turmoil period. Asset prices, interest rates and trade channel seem to play major role in propagation of monetary policy shocks.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".