FOREIGN EXPERIENCE OF USING THE DISCOUNT RATE AS A TOOL OF MACROECONOMIC REGULATION
Bibliographic record
Abstract
The shifts that took place at the beginning of the 21st century in the global financial space have clearly demonstrated the great importance of monetary policy to ensure effective economic development. This is why successful international experience in the field of monetary policy implementation is of scientific interest in order to use it in national practice. The article analyzes the current practice of monetary policy in different countries on the example of the discount rate level. Chiefly the discount rate of such countries as the Republic of Belarus, the Russian Federation, the Republic of Poland, Hungary, the Kingdom of Norway, the Republic of Bulgaria, Germany, the Czech Republic and Canada is studied for the last 10 years. The article also examines the relationship between changes in the discount rate and the dynamics of loans in Germany, the Czech Republic and Canada. In addition, the dynamics of the discount rate is compared with the dynamics of inflation in the Czech Republic and Canada. As a result, the analysis has revealed that because of the coronavirus crisis, the central banks of almost all the surveyed countries lowered the discount rate level several times to support the national financial system and economy as a whole. Equilibrium in the banking system forms the basis to create general economic equilibrium, and this is primarily achieved by predictable, appropriate and effective instruments of the state’s monetary policy. Note that based on the processing statistics, it has been found that the stable level of the discount rate is typical for the Republic of Bulgaria and Germany. Thus, these countries have the least number of changes (one change) in the discount rate level from 2015 to 2020. In turn, Canada is characterized be a particularly frequent change in the discount rate level and ambiguous dynamics of lending. To sum up, forecasts of the level and dynamics of the discount rate in the studied countries for the near future are briefly highlighted.
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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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".