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Record W2313695310 · doi:10.1257/mac.20140362

The Exchange Rate Response to Monetary Policy Innovations

2016· article· en· W2313695310 on OpenAlexaff
Viktoria Hnatkovska, Amartya Lahiri, Carlos Végh

Bibliographic record

VenueAmerican Economic Journal Macroeconomics · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCounterfactual thinkingEconomicsMarket liquidityMonetary economicsCurrencyMonetary policyExchange rateChannel (broadcasting)Developing countryTransmission channelTransmission (telecommunications)Computer science

Abstract

fetched live from OpenAlex

We present a new data fact: in response to a monetary tightening, the domestic currency tends to appreciate in developed countries but depreciate in developing countries. A model is developed to rationalize this contrasting pattern. It has three key channels of monetary transmission: a liquidity demand channel, a fiscal channel, and an output channel. The paper shows that a calibrated version of the model can explain the contrast between developed and developing countries. Using counterfactual experiments and empirical evidence, we identify differences in the liquidity demand effect as critical in explaining the contrasting responses generated by the model. (JEL E23, E43, E52, F31, F33, O19)

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.711
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.009

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 head0.031
GPT teacher head0.251
Teacher spread0.221 · 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 designNot applicable
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

Citations54
Published2016
Admission routes1
Has abstractyes

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