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Record W3122935489

Effects of Mineral-Commodity Price Shocks on Monetary Policy in Developed Countries

2014· preprint· en· W3122935489 on OpenAlexaboutno aff
A. Sekine

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2014
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsCommodityMonetary policyMonetary economicsInterest rateInflation (cosmology)Finance
DOInot available

Abstract

fetched live from OpenAlex

This paper investigates effects of changes in mineral commodity prices on monetary policy. Using macroeconomic data from five developed countries (Australia, Canada and New Zealand as mineral-producing countries, and the US and the UK as non-mineral-resource countries), I estimate the impulse response functions of the policy interest rates and the core consumer price index (CPI) inflation rates to mineral-commodity price shocks. I find that, in response to an unexpected 10 percent increase in mineral commodity prices, the central banks in the mineral-producing countries are estimated to increase their policy interest rates by approximately one percentage point, and they seem to take anticipatory policy reactions to control core CPI variations triggered by these shocks. Thus, mineral commodity prices appear to be important determinants of the monetary policies in the mineral-producing countries. However, the effects of the increase in their policy interest rates on core CPI inflation are different across the examined mineral-producing countries. I also find that the central banks in the non-mineral- resource countries insignificantly respond to mineral-commodity price shocks because such price shocks have little impact on those countries’ core CPI inflation.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.586
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.019
GPT teacher head0.246
Teacher spread0.227 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

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