The Anatomy of Financial Vulnerabilities and Crises
Bibliographic record
Abstract
We extend the framework used in Aikman, Kiley, Lee, Palumbo, and Warusawitharana (2015) that maps vulnerabilities in the U.S. financial system to a broader set of advanced and emerging economies. Our extension tracks a broader set of vulnerabilities and, therefore, captures signs of different types of crises. The typical anatomy of the evolution of vulnerabilities before and after a financial crisis is as follows. Pressures in asset valuations materialize, and a build-up of imbalances in the external, financial, and nonfinancial sectors follows. A financial crisis is typically followed by a build-up of sovereign debt imbalances as the government tries to deal with the consequences of the crisis. Our early warnings indicators which aggregate these vulnerabilities predict banking crises better than the Credit-to-GDP gap at long horizons. Our indicators also predict the severity of banking crises and the duration of recessions, as they take into account possible spill-over and amplification channels of financial stress to from one to another sector in the economy. Our indicators are of relevance for macroprudential and crisis management, in part, because they perform better than the Credit-to-GDP gap and do not suffer from the gaps econometric flaws.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".