Bibliographic record
Abstract
Central banks of the countries, by performing their basic functions, contribute to the well-being of the entire economy. One of their major functions is to conduct a monetary policy that influences both the economy and the population. This process takes place through monetary transmission channels. The effectiveness of monetary transmission channels varies between countries and it is conditioned by a number of factors that characterize the country's economic and financial system. In accordance with these factors, the monetary authorities make the choice of an adequate monetary strategy. Each monetary strategy has a number of advantages and disadvantages. In today's context, a large number of countries have chosen an inflation targeting strategy because of a number of positive macroeconomic effects, as is the case in the countries which are analyzed in this paper.\nIn order to achieve monetary policy goals that are set, the central bank takes certain measures and uses monetary policy instruments. Using monetary policy instruments, the central bank has effects on macroeconomic flows. However, these instruments influence indirectly and with some time delay on predefined goals. One of the most common goals is price stability. Because of these limitations in the operation of monetary policy it is essential to identify the monetary transmission channels in each country, so creators could determine the optimal monetary regime and could predict the impact of monetary policy on the real economy.\nIn this paper, using the vector autoregression model (VAR model), we performed an analysis of the efficiency of monetary transmission channels in selected developed and developing countries (United Kingdom, Canada, United States of America (USA), Mexico, Chile, Brazil and Serbia). This empirical analysis enabled to determine the dominant monetary transmission channels in observed countries. The results of the analysis provide guidance in taking the necessary steps to the monetary authorities, so it can reduce the time lag and eliminate restrictions on the transmission of monetary impulses to real economic quantities. However, it should be borne in mind that the efficiency of monetary transmission channels not only varies between countries, but also changes over time within a country, depending on changes in the economic system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".