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Spillover Effects of Unconventional Monetary Policy on Asia and the Pacific

2019· book-chapter· en· W3123520731 on OpenAlexaboutno aff
María Teresa Punzi, Pornpinun Chantapacdepong

Bibliographic record

VenueOxford University Press eBooks · 2019
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsSpillover effectEconomicsFinancial crisisMonetary policyInflation (cosmology)CurrencyMonetary economicsAsset (computer security)Interest rateQuarter (Canadian coin)International economicsMacroeconomicsGeography

Abstract

fetched live from OpenAlex

The chapter assesses the evolution of spillover effects of unconventional monetary policies (UMPs) and their macroeconomic impact on Asia and the Pacific region. It develops a Panel Vector Auto Regression model for a period covering data from first quarter 2000 until first quarter 2015. It finds that Asia and the Pacific region has responded to the advanced economies’ actions with accommodative monetary policy. Such lower interest rates were coupled with currency appreciation, asset price inflation, and strong movements in capital flows. If prior to the Global Financial Crisis (GFC), the ‘global saving glut’ hypothesis (i.e. Asian savings flight to the US) was one of the major effects resulting in booming US house prices, it is clear that a reversal effect has dominated the economy after the GFC: funds flight to Asia and the Pacific region putting pressure on asset prices, leading to financial vulnerability.

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)0.000
metaresearch head score (Gemma)0.002
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: Other · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.012
GPT teacher head0.175
Teacher spread0.162 · 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
GenreOther

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

Citations4
Published2019
Admission routes1
Has abstractyes

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