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Record W4393358973 · doi:10.3386/w32245

Spillovers and Spillbacks

2024· report· en· W4393358973 on OpenAlexaff
Sushant Acharya, Paolo Pesenti

Bibliographic record

VenueNational Bureau of Economic Research · 2024
Typereport
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsBank of Canada
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

We study international monetary policy spillovers and spillbacks in a tractable two-country Heterogeneous Agent New Keynesian model.Relative to Representative Agent (RANK) models, our framework introduces a precautionary-savings channel, as households in both countries face uninsurable income risk, and a real-income channel, as households have heterogeneous marginal propensities to consume (MPC).While both channels amplify the size of spillovers/spillbacks, only precautionary savings can change their sign relative to RANK.Spillovers are likely to be larger in economies with higher fractions of high MPC households and more countercyclical income risk.Quantitatively, both channels amplify spillovers by 30-60% relative to RANK.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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: Other · Consensus signal: none
Teacher disagreement score0.949
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.293
GPT teacher head0.516
Teacher spread0.224 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreOther

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

Citations1
Published2024
Admission routes1
Has abstractyes

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