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Record W2168902367 · doi:10.34989/swp-2008-6

Inflation Targeting and Price-Level-Path Targeting in the GEM: Some Open Economy Considerations

2021· preprint· en· W2168902367 on OpenAlexaffabout
Donald Coletti, René Lalonde, Dirk Muir

Bibliographic record

VenueEconstor (Econstor) · 2021
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsBank of Canada
Fundersnot available
KeywordsEconomicsInflation (cosmology)Inflation targetingPath (computing)Price levelMonetary economicsOpen economyKeynesian economicsSmall open economyMacroeconomicsMonetary policyComputer scienceExchange rate

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.337
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.081
GPT teacher head0.254
Teacher spread0.173 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2021
Admission routes2
Has abstractyes

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