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Record W2269238866 · doi:10.1017/s1365100517000177

FINANCIAL SECTOR INTERCONNECTEDNESS AND MONETARY POLICY TRANSMISSION

2018· article· en· W2269238866 on OpenAlex
Alessandro Barattieri, Maya Eden, Dalibor Stevanović

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMacroeconomic Dynamics · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsCenter for Interuniversity Research and Analysis on Organizations
Fundersnot available
KeywordsVector autoregressionStylized factEconomicsMonetary policyMonetary economicsInterest rate channelStructural vector autoregressionImpulse responseMonetary transmission mechanismAutoregressive modelFinancial acceleratorMacroeconomicsCredit channelEconometricsInflation targetingDynamic stochastic general equilibrium

Abstract

fetched live from OpenAlex

We present a stylized model that illustrates how interbank trading can reduce the sensitivity of lending to entrepreneurs' net worth, thus affecting the transmission mechanism of monetary policy through the credit channel. We build a model-consistent measure of interconnectedness and document that, in the United States, this measure has increased substantially during the period 1952–2016. Finally, interacting the measure of interconnectedness in a structural vector autoregression and a factor-augmented vector autoregression for the US economy, we find that the impulse responses of several real and financial variables to monetary policy shocks are dampened as interconnectedness increases. We confirm the same result using data from 10 Euro area countries for the period 1999–2016.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.743
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.213
Teacher spread0.202 · 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