Overleveraging, financial fragility and the banking-macro link: Theory and empirical evidence
Bibliographic record
Abstract
We investigate consequences of overleveraging and financial-sector stress on real economic activities. When banks become vulnerable, due to high leveraging, and there is a strong feedback between the real and the financial sector, a regime of high financial stress may arise. The vulnerability of the banking system in a high lever- age and a high-stress regime can, through macro feedback effects, result in unstable dynamics. To assess this question empirically, we employ a nonlinear, multi-regime vector autoregression approach (MRVAR), to explore the consequences of instabilities arising from regime dependent shocks. We analyze data on industrial production and the IMF Financial Stress Index. In order to assess how output is affected by the individual risk drivers making up the IMF index, we study eight economies - the U.S., Canada, Japan and the UK, and for the four largest euro-zone economies, namely, Germany, France, Italy, and Spain -, using Granger-causality and nonlinear impulse-response analysis. Our results strongly suggest that financial-sector stress, exerts a strong, nonlinear influence on economic activity, but that individual risk drivers affect economic activity rather differently across stress regimes and across countries.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".