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Record W1501106282 · doi:10.34989/sdp-2010-4

Prospects for Global Current Account Rebalancing

2021· preprint· en· W1501106282 on OpenAlexaffabout
Carlos de Resende, René Lalonde, Stephen Snudden

Bibliographic record

VenueEconstor (Econstor) · 2021
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsBank of Canada
Fundersnot available
KeywordsCurrent accountGlobal imbalancesFinancial crisisEconomicsCurrent (fluid)General equilibrium theoryMacroeconomicsExchange rateEngineering

Abstract

fetched live from OpenAlex

The authors use the Bank of Canada's version of the Global Economy Model, a multi-country, multi-sector dynamic stochastic general-equilibrium model with an active banking system (the BoC-GEM-FIN), to study the evolution of global current account balances following the recent global financial crisis. More specifically, they use several shocks from the model to generate a simulated baseline scenario that mimics: (i) the initial, pre-crisis state of disequilibrium in global current account balances, and (ii) the effects of the crisis, including those of the policy responses undertaken worldwide. The authors find that a sufficient set of conditions and policies for a sustainable resolution of the global current account imbalances relies on three key elements: (i) a continuous upward adjustment of U.S. private savings, (ii) fiscal consolidation in advanced countries, and (iii) an orderly adjustment of exchange rates. These three criteria facilitate a gradual decline in the U.S. current account deficit going forward. A fourth key element, the implementation of policies aimed at stimulating domestic demand in emerging Asia, is needed to ensure that the counterpart of the decrease in the U.S. current account deficit is mainly a reduction in the surpluses of emerging Asia. Sensitivity analysis based on deviations from these conditions illustrates the factors behind the main results and the costs associated with the alternative scenarios considered.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.595
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.028
GPT teacher head0.261
Teacher spread0.233 · 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 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

Citations2
Published2021
Admission routes2
Has abstractyes

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