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Record W3211678059

Drivers of consumer prices and exchange rates in small open economies

2020· preprint· en· W3211678059 on OpenAlexaboutno aff
Vesna Corbo, Paola Di Casola

Bibliographic record

VenueRePEc: Research Papers in Economics · 2020
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsExchange rateEconomicsSmall open economyShock (circulatory)Monetary economicsExchange-rate pass-throughInflation (cosmology)CurrencyOpen economyDeflationDemand shockSign (mathematics)Bayesian vector autoregressionMacroeconomicsMonetary policyBayesian probability
DOInot available

Abstract

fetched live from OpenAlex

We study the fluctuations of exchange rates and consumer prices in two small open economies with large foreign dependence, Sweden and Canada, using structural Bayesian VARs with zero and sign restrictions. For both economies, we find that the main drivers of consumer price inflation are global demand shocks, i.e. global temporary shocks that generate a positive comovement between foreign output and foreign inflation, including shocks to monetary policy abroad. Negative global demand shocks are not only deflationary for small open economies, but also induce an exchange rate depreciation. Hence, the observed exchange rate pass-through following these shocks is of opposite sign to what is usually expected. Finally, exogenous shocks to the exchange rate are found to be less important for exchange rate fluctuations than in many other structural macroeconomic models.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.176
GPT teacher head0.325
Teacher spread0.149 · 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 designObservational
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

Citations0
Published2020
Admission routes1
Has abstractyes

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