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Record W3026027840 · doi:10.17016/feds.2017.037r1

Monetary Policy and the Predictability of Nominal Exchange Rates

2017· article· en· W3026027840 on OpenAlexaff
Martin Eichenbaum, Benjamin K. Johannsen, Sérgio Rebelo

Bibliographic record

VenueFinance and Economics Discussion Series · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsPredictabilityExchange rateEconomicsInternational Fisher effectMonetary policyInflation (cosmology)Monetary economicsEconometricsSample (material)Interest rate parityInflation targetingInterest rateNominal interest rateReal interest rateStatisticsMathematics

Abstract

fetched live from OpenAlex

This paper documents two facts about countries with floating exchange rates where monetary policy controls inflation using a short-term interest rate. First, the current real exchange rate predicts future changes in the nominal exchange rate at horizons greater than two years both in sample and out of sample. This predictability improves with the length of the horizon. Second, the real exchange rate is virtually uncorrelated with future inflation rates both in the short run and in the long run. We show that a large class of open-economy models is consistent with these findings and that, empirically and theoretically, the ability of the real exchange rate to forecast changes in the nominal exchange rate depends critically on the nature of the monetary regime.

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)0.029
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 score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.241
Teacher spread0.201 · 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

Citations28
Published2017
Admission routes1
Has abstractyes

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