The impact of inflation targeting: Testing the good luck hypothesis
Bibliographic record
Abstract
Abstract Starting in the mid‐1980s, the level and volatility of inflation decreased across industrial countries. The inflation stabilization can be explained by a shift in monetary policy or by a lucky period of low volatility in business cycle shocks. To test the “good luck hypothesis,” we examine the inflation experience of Canada, one of the earliest and most successful adopters of an inflation targeting monetary policy. We Kalman‐filter the historical structural shocks consistent with an estimated dynamic stochastic general equilibrium (DSGE) model of the Canadian economy. The estimated shocks are used to build counterfactual histories. The good luck hypothesis can explain only a minor portion of the change in the path and volatility of inflation after the shift in policy. Most of inflation and output stabilization is accounted by the impact on expectations. Unconditionally, the inflation targeting policy does not improve on the previous policy in terms of inflation volatility, but supports a more favourable trade‐off, reducing substantially output volatility.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".