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Record W1531052309 · doi:10.3386/w19588

The Optimal Currency Area in a Liquidity Trap

2013· report· en· W1531052309 on OpenAlexafffund
David Cook, Michael Devereux

Bibliographic record

VenueNational Bureau of Economic Research · 2013
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaRoyal Bank of Canada
KeywordsMarket liquidityTrap (plumbing)CurrencyMonetary economicsLiquidity trapVirtual currencyEconomicsBusinessFinancial systemLiquidity riskEnvironmental science

Abstract

fetched live from OpenAlex

Open economy macro theory says that when a country is subject to idiosyncratic macro shocks, it should have its own currency and a flexible exchange rate.But recently in many countries policy rates have been pushed down close to the lower bound, limiting the ability of policy-makers to accommodate shocks, even in open economies with flexible exchange rates.In this paper, we show that if the zero bound constraint is binding and policy lacks an effective `forward guidance' mechanism, a flexible exchange rate system may be inferior to a single currency area, even when there are country-specific macro shocks.When monetary policy is constrained by the zero bound, under independent currencies with flexible exchange rates, the exchange rate exacerbates the impact of shocks.Remarkably, this may hold true even if only a subset of countries are constrained by the zero bound, and other countries freely adjust their interest rates.In order for a regime of multiple currencies to dominate a single currency area in a liquidity trap environment, it is necessary to have effective forward guidance in monetary policy.

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)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0050.006
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.652
GPT teacher head0.490
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 designTheoretical or conceptual
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

Citations5
Published2013
Admission routes2
Has abstractyes

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