Monetary Policy in a Post-Crisis World: Experiences and Practices
Bibliographic record
Abstract
In this special volume, we gave special attention to new trends in the monetary policy practice around the world. Particularly after the Subprime crisis (2008-2009), conventional monetary policy, based solely on basic interest rate adjustments, became unable to deal with problems relating economic activity, inflation, strong capital mobility, exchange rate fluctuations, public’s expectations, and asset prices volatility, among other related aspects. Since then, monetary policy has been seen as a more general strategy pursued by central banks, remarkably dependent on reputation and credibility building over time. Economic models which are conceived to analyze such a new trend in monetary policy, as well as its macroeconomic implications, present some characteristics that are not so new: rational expectations, prices and inflation rigidities in short-term and a long-term economic process in which potential output and natural unemployment levels are not correlated with the monetary policy path. Are such assumptions still plausible? Are them corroborated by empirical evidences in a robust manner? These are open questions and certainly will lead economic research in the current century. In this special volume we present a relevant sample of theoretical and empirical papers that aimed to find meaning and answers for monetary policy questions which naturally emerge in this New Global World.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".