Análisis de la relación entre el precio del cobre y el crédito en la economía peruana desde el 2004 hasta el 2020
Bibliographic record
Abstract
This paper seeks to estimate the impact that the Price of the cooper has on the private credit Dynamic for the peruvian economy. Literature shows that excessive growth of private credit could have a negative effect on the financial system and it could be even worse if said economy is commodity dependent. The effect of the so called commodity boom usually leads to an unsual increase on the private credit. In the long run, if such behavior persists, it could end up in a financial crisis. For the peruvian case, not only are its economy commodity dependant but his economy has registered a persistent growth of the credit provided to the private sector for almost 20 years. Moreover, the uncertainty caused by the trade war between USA and China has impacted the prices of some of the most important commodities to Peru and therefore had negative implications on their comercial balance. Copper demand from China has decreased, copper been the main commodity exported by Perú and China its bigger partner. It should be noticed that this paper will not take in consideration the possible effects of the SARS-CoV-2 (COVID-19). By taking in consideration the literature about financial crisis and given the peruvian economic enviroment, in this work we will focus on analysing the intenational prices of cooper, main commodity exported by Peru, and it’s relationship with the private credit. Our time period will be from 2004 to the first quarter of 2020.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".