Comprehensive Monetary Easing In The Eurozone: Lessons Learnt From Japan
Bibliographic record
Abstract
The European Central Bank’s (ECB) unconventional monetary policy has so far failed to deliver the much-anticipated results. In October 2019, the euro area’s (EA-19) HICP-inflation fell to a three-year low of just 0.7% year-over-year (y/y), thus being far below the ECB’s goal of “below, but close to 2.00% over the medium term”. By November 2019, HICP-inflation had recovered to a modest 1.0% (y/y) with seasonally-adjusted Eurozone GDP growing at a disappointing 1.2% (y/y) in Q3 2019 compared with the same quarter of the previous year (Eurostat, 2019). Inevitably, these developments raise the question to what extent the ECB might eventually consider extending its Quantitative Easing (QE) program, i.e. its €2.6tn asset purchase programs (APP) beyond the ongoing €20bn-per-month purchase of fixed income securities. Any further easing could, for example, foresee an enhancement of the securities purchased to inter alia include shares of stock. In contrast to widely held beliefs, this by no means were an entirely unprecedented phenomenon, but corresponded to measures (so-called comprehensive monetary easing, CME) adopted by the Bank of Japan (BoJ) as early as 2010 (Bank of Japan, 2010a). Notwithstanding the BoJ’s CME, however, HICP-inflation in Japan fell to 0.0% (y/y) in December 2019, the latest date for which data were available, which caused annual HICP-inflation for the full year to drop to only 0.8% (y/y). Based on the experiences gained in Japan, and notwithstanding a potential revision of the ECB’s inflation target to a 1.5% to 2.5% range, this contribution will analyse the extent to which an expansion of the ECB’s set of hitherto employed unconventional monetary policies through CME could sustainably stimulate economic growth - and inflation - in the euro area. Preliminary results suggest a rather muted impact.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".