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Record W2924671319 · doi:10.1111/apel.12254

Monetary policy responses of Asian countries to spillovers from US monetary policy

2019· article· en· W2924671319 on OpenAlexaboutno aff
Pham Thi Tuyet Trinh, Tran-Phuc Nguyen

Bibliographic record

VenueAsian-Pacific Economic Literature · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsMonetary policyEconomicsChinaVulnerability (computing)Monetary economicsEast AsiaQuarter (Canadian coin)International economicsDevelopment economicsGeography

Abstract

fetched live from OpenAlex

Regional integration in Asia has been considerably enhanced over the past 20 years or so. Whether integration helps Asian countries reduce their vulnerability to external shocks or is a channel for spreading external shocks remains an open question. This paper assesses the spillovers from US monetary policy shocks to Asian countries while taking into account country‐specific characteristics in explaining differences in timing and magnitude of responses across Asian countries. The results indicate that policy interest rates in Asian countries generally respond to innovations in the Fed rate in the same direction, but typically with a lag of one quarter. However, the size of the responses varies across Asian countries with respect to country‐specific characteristics. These results suggest that an independent monetary policy may not be feasible for an Asian developing country that adopts a pegged rate regime while being extensively integrated into the world economy. However, the hypothesis of the impossible trinity may not be relevant in the case of China.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.219
Teacher spread0.205 · 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

Citations17
Published2019
Admission routes1
Has abstractyes

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