HOUSEHOLDS' DEFAULT PROBABILITY: AN ANALYSIS BASED ON THE RESULTS OF THE HFCS*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In an environment where the Portuguese banking system has a high exposure to the household sector, identifying the households’ characteristics associated with a higher probability of default on loans is of great importance to monitor the outlook for credit risk and its consequences for the stability of the fi nancial system. This article estimates a probability of default for households which depends on their economic and socio-demographic characteristics and takes into account the existence of shocks that adversely affected their fi nancial situation. The estimated probability is used to characterize the distribution of credit risk for some household’s groups, which differ on their situation in the debt market, and for different types of loans. The analysis uses data from the Household Finance and Consumption Survey which took place during the second quarter of 2010.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it