In‡ation Targeting: What Have We Learned
Bibliographic record
Abstract
Inflation targeting has been widely adopted in both developed and emerging economies. In this essay, I survey the evidence on the effects of inflation targeting on macroeconomic performance and assess what lessons this evidence provides for inflation targeting and the design of monetary policy. While macroeconomic experiences among both inflation targeting and non-targeting developed economies have been similar, inflation targeting has improved macroeconomic performance among developing economies. Importantly, inflation targeting has not been associated with greater real economic instability among either developed or developing economics. While cost shocks, such as the large rise in commodity prices that occurred in 2007 and early 2008, force central banks to make difficult short-run trade-offs, the ability to deal with demand shocks and financial crises can be enhanced by a commitment to an explicit target. This article was adapted from the John Kuszczak Memorial Lecture, prepared for the conference on ‘International Experience with the Conduct of Monetary Policy under Inflation Targeting’, held at the Bank of Canada, 22–23 July 2008. I would like to thank Mahir Binici for excellent research assistance and conference participants and an anonymous referee for comments and suggestions. Views expressed and remaining errors are my own.
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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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".