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Record W2998028790 · doi:10.1080/08911916.2019.1693164

The Illusion of Inflation Targeting: Have Central Banks Figured Out What They Are Actually Doing Since the Global Financial Crisis? An Alternative to the Mainstream Perspective

2019· article· en· W2998028790 on OpenAlexaff
Mario Seccareccia, Najib Khan

Bibliographic record

VenueInternational Journal of Political Economy · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsFinancial crisisEconomicsInflation (cosmology)Inflation targetingMandateMonetary policyInterest rateMainstreamAusterityMonetary economicsEuropean debt crisisPost-Keynesian economicsMacroeconomicsKeynesian economicsFinancial systemPoliticsEconomic policyPolitical science

Abstract

fetched live from OpenAlex

Current discussions over the behavior of central banks show that more and more political leaders are demanding that the monetary authorities abandon a single-goal mandate of solely combating inflation. Many are considering a multi-goal commitment that would include not only concern with inflation, as had been the case before the global financial crisis. Central banks should also give due consideration to the problem of unemployment, income distribution and macro-prudential risks in their interest-rate setting. By looking at the experience of fourteen inflation-targeting countries since the global financial crisis, empirical evidence suggests that central banks have shifted significantly their behavior and have shown a high degree of pragmatism in dealing with the aftermath of the financial crisis, by loosening their focus on inflation. Using an alternative post-Keynesian analytical framework, the article then proceeds with an analysis of how central banks can effectively achieve a multi-goal commitment that would include full employment and a more equitable distribution of income in their pursuit of monetary policy.

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)0.001
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.134
Threshold uncertainty score0.474

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.014
GPT teacher head0.270
Teacher spread0.256 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations14
Published2019
Admission routes1
Has abstractyes

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