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Record W1557652280

Official Japanese Intervention in the JPY/USD Exchange Rate Market: Is It Effective and Through Which Channel Does It Work?

2009· preprint· en· W1557652280 on OpenAlexaff
Rasmus Fatum

Bibliographic record

VenueEconstor (Econstor) · 2009
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIntervention (counseling)SpeculationPortfolioChannel (broadcasting)Psychological interventionBalance (ability)Sample (material)Exchange rateWork (physics)BusinessEconomicsMonetary economicsMedicineTelecommunicationsComputer scienceFinanceEngineering
DOInot available

Abstract

fetched live from OpenAlex

This paper investigates whether official Japanese intervention in the JPY/USD exchange rate over the January 1999 to March 2004 time period is effective. By integrating the official intervention data with a comprehensive set of newswire reports capturing days on which there is a rumor or speculation of intervention, the paper also attempts to shed some light on which of the two channels, the signaling channel in a broad sense or the portfolio balance channel, effective Japanese intervention works through. The results suggest that Japanese intervention is effective during the first five years of the sample and ineffective during the last three months of the sample, thereby providing an ex post rationale for why Japan intervened as well as for why the interventions stopped. Moreover, the results suggest that when Japanese intervention is effective, it works through a portfolio balance channel. The results do not rule out that effective intervention also works through signaling.

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)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.044
GPT teacher head0.255
Teacher spread0.211 · 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 designSimulation or modeling
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

Citations11
Published2009
Admission routes1
Has abstractyes

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