Анализ финансовой стратегии Банка России в части воздействия ставки рефинансирования (ключевой ставки) на российскую экономику
Bibliographic record
Abstract
Aim. This study aims to analyze the financial strategy of the Bank of Russia in terms of the quantitative effect of bank rate (key interest rate) on the Russian economy based on econometric modeling. The conclusions obtained can be used to optimize the financial strategy of the central bank in terms of key interest rate and its effect on the Russian economy. Materials and methods. This study introduces a computer technology for the creation and evaluation of simultaneous equations according to the basic indicators of the Russian macroeconomy and key interest rate using the econometric software package Stata. The analysis is based on the official quarterly data provided by government statistics and the Bank of Russia from the fourth quarter of 1994 to the fourth quarter of 2014 (81 quarters), which allows a quantitative assessment of the financial strategy of the central bank. Results. Analysis of the quantitative effect of bank rate (key interest rate) on the Russian economy using econometric modeling, i. e., evaluation of simultaneous equations according to the basic indicators of the Russian macroeconomy and key interest rate, reveals that this effect is far from optimal. For example, the effect of the average weighted quarterly bank rate (key interest rate) from the fourth quarter of 2000 to the fourth quarter of 2014 results in a lower average quarterly contribution to the aggregate income of the population. The population loses an average of 26,991 billion rubles at 2014 values quarterly, which decreases consumption and GDP. Meanwhile, the negative effect of the bank rate on the chain index is minimal.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".