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Record W4285617221 · doi:10.3386/w28079

Sudden Stops and Optimal Foreign Exchange Intervention

2020· report· en· W4285617221 on OpenAlexaff
J. Scott Davis, Michael Devereux, Changhua Yu

Bibliographic record

VenueNational Bureau of Economic Research · 2020
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsForeign exchangeIntervention (counseling)MedicineMonetary economicsEconomicsPsychiatry

Abstract

fetched live from OpenAlex

We model sudden stops in a small open economy as rare discrete events precipitated by increases in the world risk-free rate.When external debt is large, the model exhibits multiple equilibria, one where external debt and consumption remain high, and one with a collapse in external debt and consumption.Private agents delever following an increase in the world interest rate, but they fail to internalize the impact of deleveraging on the price of collateral.For high levels of debt, even a small increase in the world interest rate can eliminate the high debt equilibrium and the economy experiences a sudden stop.The central bank can use foreign exchange intervention to prevent the sudden stop.If reserves cannot be borrowed, optimal policy is to "lean against the wind", buying foreign reserves ex-ante when private borrowing is high and selling them after an interest rate shock when private agents are deleveraging.

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)0.006
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 score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.390
GPT teacher head0.473
Teacher spread0.082 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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