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Record W1556111450 · doi:10.34989/swp-2003-29

Nominal Rigidities and Exchange Rate Pass-Through in a Structural Model of a Small Open Economy

2021· preprint· en· W1556111450 on OpenAlexaffabout
Steve Ambler, Ali Dib, Nooman Rebei

Bibliographic record

VenueRePEc: Research Papers in Economics · 2021
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsBank of CanadaUniversité du Québec à Montréal
Fundersnot available
KeywordsExchange-rate pass-throughExchange rateEconomicsSmall open economyOpen economyRigidity (electromagnetism)Monetary economicsMacroeconomicsEconomyEconometricsEngineering

Abstract

fetched live from OpenAlex

The authors analyze exchange rate pass-through in an estimated structural model of a small open economy that incorporates three types of nominal rigidity (wages and the prices of domestically produced and imported goods) and eight different structural shocks. The model is estimated using quarterly data from Canada and the United States. It predicts a remarkably similar dynamic relationship between the nominal exchange rate and prices in response to the different structural shocks: the nominal exchange rate overshoots its long-run level, and changes in the nominal exchange rate are passed through slowly to the domestic price level. The authors show that, although pricing to market (the slow adjustment of the domestic-currency prices of imported goods) is necessary to generate slow pass-through to the prices of imported goods, it is not necessary to generate slow pass-through to the overall price level. Sticky domestic wages also generate slow exchange rate pass-through, even when the prices of imported goods adjust instantaneously to changes in the exchange rate.

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)0.008
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.180
GPT teacher head0.321
Teacher spread0.141 · 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

Citations36
Published2021
Admission routes2
Has abstractyes

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