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Record W3124378932

Learning and the Welfare Implications of Changing Inflation Targets

2005· article· en· W3124378932 on OpenAlex
Kevin Moran

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCahiers de recherche · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsEconomicsRobustness (evolution)Inflation (cosmology)WelfareMonetary policyEconometricsSteady state (chemistry)Bayesian probabilityReal interest rateMonetary economicsMathematicsStatisticsPhysics
DOInot available

Abstract

fetched live from OpenAlex

This paper computes the welfare consequences, for a representative agent, of a shift in the inflation target of monetary authorities. The welfare computations are conducted first by comparing the two steady states that the different inflation targets entail, and next by accounting for the transition from one steady-state to the next. Further, the paper allows this transition to be characterized by incomplete information, under which private agents learn about the inflation target shift using Bayesian updating. The analysis is repeated in a variety of model parameterizations, to test the robustness of the results.\nWe find that the welfare benefits of reducing the target rate of inflation from 2% initially to 0% may at first appear to be significant. When measured by comparing steady states, these benefits are worth up to 0.5% of steady-state consumption. However, accounting for the transition towards the new, low inflation steady state significantly reduces the computed benefits, by at least one half.

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.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.588
Threshold uncertainty score0.253

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.134
GPT teacher head0.301
Teacher spread0.167 · 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