Shadow rates as a measure of the monetary policy stance: Some international evidence
Bibliographic record
Abstract
Abstract This paper examines the usefulness of shadow rates to measure the monetary policy stance by comparing them to the official policy rates and those implied by three types of Taylor rules in both inflation‐targeting countries (the UK, Canada, Australia and New Zealand) and others that have only targeted inflation at times (the United States, Japan, the Euro Area and Switzerland) over the period from the early 1990s to December 2021. Shadow rates estimated from a dynamic factor model are shown to suggest a much looser policy stance than either the official policy rates or those implied by the Taylor rules, and generally to provide a more accurate picture of the monetary policy stance during both ZLB and non‐ZLB periods, since they reflect the full range of unconventional policy measures used by central banks. Furthermore, generalised impulse response analysis based on three alternative vector autoregression (VAR) models indicates that monetary shocks based on the shadow rates are more informative than those related to the official policy rates or to two‐ and three‐factor shadow rates, especially during the Global Financial Crisis and the recent COVID‐19 pandemic, when unconventional measures have been adopted. Finally, unconventional policy shocks seem to have less persistent effects on the economy in countries, which have adopted an inflation‐targeting regime.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".