Differential Effect on the Determinants of the Late Payments According to the Economic Cycle
Bibliographic record
Abstract
From 2004 to 2007, the Spanish financial system grew at a very rapid rate. However, from 2008 to 2012 impairment losses amounted equivalent to 25% of Spanish GDP.The objective of this paper is investigate how have affected the main problems that have suffered the Spanish economy on the late payments on Spanish credit institutions. And to check whether the effect of these problems is different on stages of growth with respect to the stages of crisis.The main results, according the test Chow, confirmed that there is a structural break in the model in 2007. Also the increase in unemployment increases the late payments and this increase is greater in the stages of growth compared to the stages of recession. The decrease in the price of housing increases the late payments. The increase in equity decreases the late payments, in these two variables decrease is greater in the recession stages compared to the growth stages.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".