On The Determinants of Credit Crunch in Italy: An Empirical Analysis on the Reasons Underlying This Phenomenon
Bibliographic record
Abstract
The purpose of this paper is to investigate whether a credit crunch occurred in Italy during the recent financial crisis and to analyze the underlying factors. In order to disentangle credit supply and demand we specify a theory-based on the time series model of the Italian credit market. After discussing the contributions of the economic literature on the phenomenon of the credit crunch and analyzed the case of Italy, we have developed an empirical analysis of an econometric model. In particular, starting from the contribution of Schmidt and Zwick (2012) regress each independent variable considered in the model based on the results of the correlogram, we will apply the logarithmic first differences and enrich the model through the use of the Kalman’ filter. As regards the demand side we have that the gross domestic product and the volume of loans issued in the quarter prior to the quarter analyzed are the most significant variables in determining the demand for credit; instead, with regard to supply, we have that the variables that most influence this component of the credit market are past the volume of credit and the differential between the interest rates.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".