Assessment of demand, supply and equilibrium price on the deposit market of Ukraine
Bibliographic record
Abstract
The paper evaluates the process of modeling of equilibrium price on the deposit market of Ukraine in the period 2005-2016 by means of formalization of the functions of the market supply and demand.By assessing the credit and deposit markets around the world in the period before the crisis and the post-crisis period, we decided to use a multivariate regression analysis to study the deposit market in Ukraine.We formed an input array of 32 independent variables, through which, by means of the method of principal component analysis and correlation analysis, we obtained 7 of the most relevant variables.After that we received two specifications of the models of supply and demand, which were conferred to the same status in order to calculate equilibrium price on the deposit market of Ukraine.The obtained values of the equilibrium price (deposit rates) amounted to 7.82%, which corresponds to three points of time in the studied period: the second quarter of 2007; the fourth quarter of 2010the first quarter of 2011 and the third quarter of 2011the fourth quarter of 2010.However, this balance does not reflect a stable situation on the deposit market of Ukraine, as it has a short-term nature.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".