Has the link between inflation uncertainty and interest rates changed after inflation targeting?
Bibliographic record
Abstract
Purpose The purpose of this paper is to establish a link between inflation uncertainty and interest rates for five inflation‐targeting countries. Design/methodology/approach The approach takes the form of a time‐varying parameter model with a Generalized Autoregressive Conditional Heteroskedasticity (GARCH) specification, used to derive impulse uncertainty and structural uncertainty. Findings This study attempts to establish a link between inflation uncertainty and interest rates for five inflation‐targeting countries, i.e. Canada, Finland, Spain, Sweden, and the UK. Decomposing inflation uncertainty into two components – impulse and structural, a positive association was found between the expected inflation and interest rates. Structural uncertainty has a positive and significant effect on interest rates for some countries. It has also been found that the long‐run effects of inflation on interest rates are less than unity for the post‐inflation targeting period, which implies that in some respect the Central Bank has been successful in targeting inflation. This has allowed the Central Bank to employ a less restrictive monetary policy in an environment of a credible inflation‐targeting strategy. Research limitations/implications Exponential Generalized Autoregressive Conditional Heteroskedasticity (EGARCH) can be used instead of GARCH modelling. Originality/value This is the first study that has tried to establish the link between different types of inflation uncertainty and interest rates for the inflation‐targeting countries to see the effect of inflation targeting.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".