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Record W1571400837 · doi:10.3386/w8559

Endogenous Currency of Price Setting in a Dynamic Open Economy Model

2001· report· en· W1571400837 on OpenAlexafffund
Michael Devereux, Charles Engel

Bibliographic record

VenueNational Bureau of Economic Research · 2001
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaNational Science Foundation
KeywordsEconomicsCurrencyOpen economySmall open economyMonetary economicsMacroeconomicsMicroeconomicsEconometricsMonetary policyExchange rate

Abstract

fetched live from OpenAlex

Many papers in the recent literature in open economy macroeconomics make different assumptions about the currency in which firms set their export prices when nominal prices must be preset.But to date, all of these studies take the currency of price setting as exogenous.This paper sets up a simple two-country general equilibrium model in which exporting firms can choose the currency in which they set prices for sales to foreign markets.We make two alternative assumptions about the structure of international financial markets: one where there are complete markets for hedging consumption risk internationally, and the other without risk-sharing possibilities.Our results are quite sharp: exporters will generally wish to set prices in the currency of the country that has the most stable monetary policy.When monetary stability is similar among countries, there is an equilibrium where firms from all countries set their price in the currency of the buyer (local currency pricing).But except for a special case where money variances are exactly identical across countries, there is no equilibrium where all firms set export prices in their own currencies (producer currency pricing).

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)0.001
metaresearch head score (Gemma)0.004
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: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0060.005
Open science0.0020.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.624
GPT teacher head0.494
Teacher spread0.129 · 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

Citations89
Published2001
Admission routes2
Has abstractyes

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