Bibliographic record
Abstract
This dissertation attempts to analyze the impact of transparency and credibility of central banks on financial markets. While the topic of central bank's transparency has been extensively elaborated in literature, the linkage between institutional framework and empirical characteristics of transparency and credibility have not been adequately described. The study aims to overcome this absence and try to quantify transparency and credibility of central banks, as well as to analyze the relationship between them and their effect on interest rates and inflation expectations. The analysis is performed for nine major central banks: the Reserve Bank of Australia, the Bank of Canada, the European Central Bank (ECB), the Bank of Japan, the Reserve Bank of New Zealand, the Sveriges Riksbank, the Swiss National Bank, the Bank of England, and the U.S. Federal Reserve, and three central and eastern European central banks--the National Bank of Poland, the Hungarian National Bank and the Czech National Bank (CNB). The transparency index, based on survey, is constructed for all twelve central banks mentioned above, while measures of transparency and credibility that are derived from financial market instruments are performed only for the ECB, Fed and the CNB. Finally, we analyze the impact of transparency and credibility of the CNB on interest rates and inflation expectations. The analysis shows that central bank transparency has improved substantially in the last decade. The most transparent central banks in our sample are the Sveriges Riksbank and the Czech National Bank, followed by the Bank of England. A range of communication channels that improve the transparency of monetary policy, however, have little impact on central bank credibility. It is primarily policy-makers' verbal comments, monetary policy implementation and clarity that affect the predictability of monetary policy and contribute most to increasing its credibility. Therefore, the main reason of low credibility is confusing communication, which cannot be eliminated even by maximum openness of the central bank. The additional study of the Czech National Bank showed that during the European debt crisis, greater credibility of the CNB reduced the uncertainty in financial market and contributed to efficient formation of private agents' inflation expectations. Moreover, the results of the analysis show that inflation expectations in the Czech Republic are formed rationally, i.e. they are based on the CNB inflation forecast rather than on the actual values of inflation.
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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 | 0.013 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".