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Record W3125040439 · doi:10.24149/gwp198

Exchange Rate Flexibility under the Zero Lower Bound

2014· article· en· W3125040439 on OpenAlexafffund
David Cook, Michael B. Devereux

Bibliographic record

VenueFederal Reserve Bank of Dallas, Globalization and Monetary Policy Institute Working Papers · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsUniversity of British Columbia
FundersEconomic and Social Research CouncilSocial Sciences and Humanities Research Council of CanadaRoyal Bank of Canada
KeywordsZero lower boundExchange-rate flexibilityMonetary economicsEconomicsExchange rateConstraint (computer-aided design)CurrencyUpper and lower boundsZero (linguistics)Monetary policyInternational Fisher effectOrder (exchange)Flexibility (engineering)Nominal interest rateInterest rateLimitingExchange-rate regimeInternational economicsReal interest rateMathematicsFinance

Abstract

fetched live from OpenAlex

An independent currency and a flexible exchange rate generally helps a country in adjusting to macroeconomic shocks. But recently in many countries, interest rates have been pushed down close to the lower bound, limiting the ability of policy-makers to accommodate shocks, even in countries with flexible exchange rates. This paper argues 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. With monetary policy constrained by the zero bound, under 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 under an optimal targeting rule. In a zero lower bound environment, in order for a regime of multiple currencies to dominate a single currency, 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 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.757
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.070
GPT teacher head0.265
Teacher spread0.195 · 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 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

Citations7
Published2014
Admission routes2
Has abstractyes

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