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Record W2259863159 · doi:10.1017/s1365100516000614

AGENCY COSTS, RISK SHOCKS, AND INTERNATIONAL CYCLES

2017· article· en· W2259863159 on OpenAlex
Marc‐André Letendre, Joël Wagner

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 · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsBank of CanadaMcMaster University
Fundersnot available
KeywordsEconomicsBusiness cycleVolatility (finance)Investment (military)Consumption (sociology)Monetary economicsEconometricsUncorrelatedAgency (philosophy)Macroeconomics

Abstract

fetched live from OpenAlex

We add agency costs into a two-country, two-good international business-cycle model. In our model, changes in the relative price of investment arise endogenously. Despite the fact that technology shocks are uncorrelated across countries, the relative price of investment is positively correlated across countries in our model, much as it is in detrended U.S./Euro-area data. We also find that financial frictions tend to increase the volatility of the terms of trade and the international correlations of consumption, hours worked, output, and investment. We then compare this model to an alternative model that also includes risk shocks. We use credit spread data (for the United States) to calibrate the AR(1) process for risk shocks. We find that risk shocks are too small to significantly impact the model's dynamics.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
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 studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.040
GPT teacher head0.250
Teacher spread0.210 · 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