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Record W2802819481 · doi:10.5539/ijef.v10n6p127

The Making of Contemporary Australian Monetary Policy - Backward- or Forward- Looking?

2018· article· en· W2802819481 on OpenAlexvenueno aff
Ying Chen, Hanyang Zhang, Kwok-Leung Tam, Maoguo Wu

Bibliographic record

VenueInternational Journal of Economics and Finance · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsMonetary policyEconomicsInflation (cosmology)Inflation targetingDeregulationForward guidanceMonetary hegemonyFunction (biology)Monetary economicsCredit channelMacroeconomics

Abstract

fetched live from OpenAlex

Monetary authorities rarely disclose the true reasons behind their policy reactions. A tracing of the policy reaction function to see if the monetary authority is applying simple rules holds the potential to offer profound insight into the past behavioral relationship between the monetary authority and economic agencies. A reasonable body of knowledge about the direction of monetary policy would, moreover, assist economic agencies in forming their expectations, which would in turn, be useful for the monetary authority in anticipating the likely trends of the overall economy. The main objective of this study is to track de Brouwer and Gilbert (2005) from the Australian financial deregulation era (from 1983 to 2002) to the present. Empirical findings show that the Reserve Bank of Australia (RBA) is more forward-looking when formulating monetary policy rather than backward-looking, and that inflation targeting plays a significant role in stabilizing the output of the economy.

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)0.006
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.005
Scholarly communication0.0060.004
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0030.000

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 head0.072
GPT teacher head0.280
Teacher spread0.208 · 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 designNot applicable
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

Citations0
Published2018
Admission routes1
Has abstractyes

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