The Relationship between Monetary Policy and Uncertainty in Advanced Economies: Evidence from Time- and Frequency-Domains
Bibliographic record
Abstract
In this work we offer new insight into the relationship between interest rates and uncertainty for several advanced economies (Canada, EU, Japan, UK, US) for the period 2003-2018. For this purpose, we utilize the wavelets methodology, which allows us to analyze how the relationship changes over time and across different frequencies and to make inference about causality. To analyze a wide range of frequencies, and because our analysis contains the post-2008 period as well, we use the daily shadow interest rate measure of Krippner (2012, 2013) to capture the stance of monetary policy making at the zero lower bound. We also use the daily uncertainty measure by Scotti (2016), which measures uncertainty related to the real economy. Our findings suggest that there is significant comovement across time and across different frequencies in all the countries we analyze. Corresponding to the similar, yet different conduct of monetary policy, we also find that the relationship exhibits different characteristics and causality in all the economies we analyze, implying that one must be careful not to draw generalized conclusions.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".