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Record W7070955053

Piecing the puzzle : real exchange rates and long-run fundamentals

2024· article· en· W7070955053 on OpenAlexaboutno aff

Bibliographic record

VenueThe central bank of Norways Open Archive · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsnot available
Fundersnot available
KeywordsPurchasing power parityExchange rateDepreciation (economics)Interest rate parityBayesian probabilityMonetary policyProductivityPurchasing powerReal interest rate
DOInot available

Abstract

fetched live from OpenAlex

This paper examines the structural determinants of real exchange rates, emphasizing the persistent low-frequency movements that traditional models, such as Purchasing Power Parity (PPP) and Uncovered Interest Parity (UIP), often fail to capture. To address this, we propose a structural VAR model with common trends, enabling a clear distinction between transitory and long-term effects of structural shocks. Estimated using Bayesian techniques and applied to Canada and Norway - two resourcerich economies - the model reveals that productivity shifts and commodity market trends significantly influence domestic activity and the real exchange rate in both countries. Importantly, the model also avoids the delayed overshooting puzzle commonly associated with recursive VARs in response to monetary policy shocks. Instead, it generates exchange rate dynamics consistent with the UIP hypothesis, characterized by immediate overshooting followed by a gradual depreciation to equilibrium.

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)0.009
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 score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0030.004
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.014
GPT teacher head0.289
Teacher spread0.275 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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