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Record W2279588514

Inflation Targeting and Economic Reforms in New Zealand

2018· article· en· W2279588514 on OpenAlexaff
Matteo Cacciatore, Fabio Ghironi, Stephen J. Turnovsky

Bibliographic record

VenueInternational journal of central banking · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicNew Zealand Economic and Social Studies
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsEconomicsInflation targetingUnemploymentMonetary policyInflation (cosmology)Monetary economicsMacroeconomics
DOInot available

Abstract

fetched live from OpenAlex

We study the consequences of economic reforms in New Zealand since the beginning of the 1990s. Inflation targeting became the monetary policymaking framework of the RBNZ in 1990. In the years that followed, New Zealand implemented labor market reform and became increasingly integrated in world trade. We use a New Keynesian model with rich trade microfoundations and labor market dynamics to study the per-formance of inflation targeting versus alternative monetary policy rules for New Zealand in relation to these market charac-teristics and reforms. We show that nominal income targeting would have been a better choice than inflation targeting or price-level targeting prior to market reforms by delivering more stable unemployment dynamics in a distorted economic envi-ronment. Nominal income targeting would also have been bet-ter than inflation targeting with respect to the transition costs ∗This is a revised version of the paper presented at the IJCB-RBNZ

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score0.735

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.017
GPT teacher head0.238
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2018
Admission routes1
Has abstractyes

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