Bibliographic record
Abstract
This doctoral dissertation studies questions related to monetary policy and its economic effects. Particularly, the focus is on questions that have been relevant from the point of view of both practical policymaking as well as academic research in the recent decades. The dissertation consists of an introductory chapter and three distinct research essays. The two first essays are empirical, while the third one is more theoretical in nature. The first essay examines the role of stock and currency market information in the monetary policy rules of 14 OECD countries during the period of 1999– 2016. The results show that both the stock market as well as currency market variables have been statistically significant predictors in the monetary policy rules of several OECD countries. Additionally, the results indicate that stock and currency market variables have had significance for monetary policy through their potential role in providing information about future economic activity. The second essay examines the influence of United States monetary policy on the monetary policies of four small open economies. The results indicate that US monetary policy has systematically influenced the monetary policies especially in Canada and the UK. Moreover, surprise developments in US monetary policy are shown to affect the interest rates in Canada, Norway, and Sweden, although the strength of this result is somewhat dependent on the empirical method used. In addition, the essay studies whether the small open economies have utilised foreign exchange interventions to enhance their monetary policy autonomy. This hypothesis is not supported in light of the empirical results of the study. The third essay builds a theoretical general equilibrium model that analyses the extent to which the economic effects of the central bank’s quantitative easing are dependent on the fiscal policy conducted by the government. It is shown that the effectiveness of quantitative easing depends on whether the government adjusts its budget constraint through changes in taxation or by issuing debt. In the latter scenario, it is shown that quantitative easing is more effective when the government is issuing bonds of long maturity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.030 | 0.011 |
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".