Strict and Flexible Inflation Forecast Targets: An Empirical Investigation
Bibliographic record
Abstract
We examine whether models of inflation forecast targeting are consistent with the observed behaviour of the central banks of Australia, Canada, and the United States. The target criteria from these models restrict the conditionally expected paths of variables targeted by the central bank, in particular inflation and the output gap. We estimate various moment conditions, providing a description of monetary policy for each central bank under different maintained hypotheses. We then test whether these estimated conditions satisfy the predictions of models of optimal monetary policy. The overall objective is to examine the extent to which and the manner in which these central banks successfully balance inflation and output objectives over the near term. For all three countries, we obtain reasonable estimates for both the strict and flexible inflation forecast targeting models, though with some qualifications. Most notably, for Australia and the United States there are predictable deviations from forecasted targets, which is not consistent with models of inflation targeting. In contrast, the results for Canada lend considerable support to simple models of flexible inflation forecast 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 | 0.008 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".