Inflation Targeting and Policy Horizons
Bibliographic record
Abstract
This study explores the role of flexibility in inflation targeting from two distinct perspectives. Using a qualitative approach, the first component summarizes and evaluates the monetary policy frameworks of inflation-targeting central banks across the industrialized world, focusing on the communication of monetary policy through framework documents and flagship publications. After establishing the core tenets of modern inflation targeting and key differences between the current frameworks of central banks within the OECD, I provide a detailed review of the Bank of Canada's experience with inflation targeting. I find that the role of flexibility within the Bank's policy framework has evolved in response to key events such as the 2008-09 financial crisis and the Covid-19 pandemic. The second component evaluates whether changes in the Bank of Canada's inflation targeting framework can be identified in the empirical evidence. Using conditional forecasts from a univariate model of inflation, I estimate policy horizons - the period over which forecasted inflation returns to target following a shock - from 2000 to 2022. I find that for a particular set of criteria for convergence, the range of estimated policy horizons is consistent with the Bank's stated target horizon of 6 to 8 quarters. Additionally, the distribution of estimates reflects a narrow type of flexibility in which the lengths of policy horizons vary based on the current level of inflation and the persistence of recent shocks. Estimating unique models for sample periods corresponding to the tenures of former Bank of Canada Governors Dodge, Carney, and Poloz, I find that there has been some variation in the behaviour of estimated policy horizons over time, which may reflect changes in central bank behaviour, changes in the economic environment, or a combination of both factors.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".