Charge-offs, Defaults and the Financial Accelerator
Bibliographic record
Abstract
Abstract U.S. banks countercyclically vary the ratio of charge-offs to defaulted loans (COD) and the standard deviation of COD is roughly 15 times that of GDP. We show that canonical financial accelerator models cannot explain these facts, but introducing stochastic default costs and stochastic risk can potentially resolve the discrepancy. Estimating the augmented model and including both surprise and news shocks reveals that default cost news shocks account for most of the variance of COD. Also, in the many model specifications we work with, default cost news shocks always account for at least 20 percent of the variance of investment, while risk news shocks account for a significant portion of the variation in the credit spread, and around 10 percent of the variation in investment growth. Both news shocks also account for a material amount of the variance of hours and output growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".