Bibliographic record
Abstract
The recent theoretical and empirical literature on monetary policy rules has increasingly focused on short-term interest rates rather than monetary aggregates for studying European monetary policy issues. There are several reasons for this: first, as in the United States, monetary aggregates in Europe have displayed a less obvious link to real economic activity and inflation during the 1980s and 1990s as opposed to the 1960s and 1970s. Second, many central banks have de-emphasised the role of monetary aggregates and have moved to operating procedures that focus more on interest rates (i.e. the Fed funds target rate in the United States) or inflation rates (i.e. the inflation targets in the United Kingdom, Canada, or New Zealand). Following the paper by Taylor (1993) and more recent applications by Clarida and Gertler (1997), Clarida, Gali and Gertler (1997, 1998), Gerlach and Smets (1998), Kuttner and Posen (1998) and Rudebusch and Svensson (1998) there is now a growing literature on so-called ‘interest rate smoothing’ rules for Europe. 1 These papers use a simple policy reaction function in which interest rate adjustment towards equilibrium depends on the deviations of inflation and output from their respective target values. It is shown that such policy reaction functions fit the data quite well. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".