Inflation, Learning and Monetary Policy Regimes in The G-7 Economies
Bibliographic record
Abstract
In this paper, the authors report estimates of two- and three-state Markov switching models applied to inflation, measured using consumer price indexes, in the G-7 countries. They report tests that show that two-state models are preferred to simple one-state representations of the data, and argue that three-state representations are more satisfactory than two-state representations for some countries. The preferred estimation results usually include a state that features a unit root in its dynamic structure, which concurs with results of direct tests for this property. However, the multistate representation of the data shows that for all G7 countries these quasi-unit-root properties arise primarily from a few brief episodes of history, concentrated in the 1970s and associated with the major oil-price shocks. For all countries there is evidence of progress towards establishing credibility of regimes with stable inflation, and in many countries there is evidence of progress in building credibility of regimes with low inflation. Credibility refers to the ex post probability assigned to the state by the Markov model, which has a large effect on how expectations of future inflation are formed. An interesting contrast arises from the results for the United States and Canada. Whereas in Canada the credibility of a regime with historically low inflation has risen sharply in the last few years, in the United States there has been convergence on a regime with a stable, but historically average, rate of inflation and not on the alternative low-inflation regime.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".