The International Finance Multiplier in Business Cycle Fluctuations
Bibliographic record
Abstract
In the wake of the gGreat Recession h of 2007-09, recent studies have emphasized the importance of the ginternational finance multiplier (IFM) h mechanism for inter- national business cycles, using calibrated two-country models. This paper develops and estimates a two- country model with the IFM mechanism using 21 time series from the Euro Area (EA) and the US. The estimation results show that during the past quarter-century, EA shocks to the external finance premium and net worth not only had a considerable effect on the EA economy together with an EA neutral technology shock, but also were transmitted to the US through the IFM mechanism and had a great impact on the US economy together with a US marginal efficiency of investment (MEI) shock. The rate of EA neutral technological change and the US MEI shock then have strong correlations with lending attitudes of banks in the EA and the US, and thus the EA neutral technology shock and the US MEI shock are likely to represent disturbances to the banking sectors in the EA and the US. These findings therefore demonstrate that financial factors are important sources of EA and US business cycle fluctuations over the past quarter-century.
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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 | 0.006 |
| 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".