Inflation Targeting and Price-Level-Path Targeting in the GEM: Some Open Economy Considerations
Bibliographic record
Abstract
This paper compares the performance of simple inflation targeting (IT) and price-level path targeting (PLPT) rules to stabilize the macroeconomy, in response to a series of shocks, similar to those seen in Canada and the United States over the 1983 to 2004 period. The analysis is conducted in a two-country (Canada and the United States), two-sector (tradables and nontradables) version of the International Monetary Fund’s Global Economy Model (GEM). The authors conclude that PLPT is slightly preferred to IT for delivering macroeconomic stability, as it delivers a reduction in inflation and nominal interest rate volatility, at the expense of slightly higher output gap variability. When the analysis is restricted to the shocks that have been most important for explaining movements in Canada’s terms of trade over this period, PLPT is still preferred to IT. The authors also show that their results are sensitive to the interaction between the relative importance of the different types of macroeconomic shocks that hit the economy, and the extent to which price and wage setting is forward looking. Lastly, the authors demonstrate that the choice of monetary policy framework in the United States does not affect the relative merits of PLPT versus IT in Canada.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".