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Record W3088357867 · doi:10.1016/j.jedc.2020.103997

The extensive margin and US aggregate fluctuations: A quantitative assessment

2020· article· en· W3088357867 on OpenAlex
Miguel Casares, Hashmat Khan, Jean‐Christophe Poutineau

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

VenueJournal of Economic Dynamics and Control · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsCarleton University
FundersUniversidad Pública de Navarra
KeywordsStylized factEconomicsBusiness cycleDynamic stochastic general equilibriumMargin (machine learning)Monetary economicsDividendRecessionProductivityRisk premiumEconometricsMonetary policyMacroeconomicsFinance

Abstract

fetched live from OpenAlex

We report empirical evidence indicating that US net business formation has recently turned more volatile, procyclical and persistent. To study these stylized facts, we estimate a DSGE model with endogenous entry and exit. Business units feature heterogeneous productivity and they shut down if the present value of expected future dividends falls below the current liquidation value. The model provides a better fit than a constant exit rate model with the fluctuations of US business formation. The introduction of the extensive margin amplifies the effects of technology and risk-premium shocks, and reduces the procyclicality of firm-level production. The main sources of variability of the US aggregate fluctuations during the Great Recession are countercyclical technology shocks, persistent adverse risk-premium shocks, and expansionary monetary policy shocks.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.895
Threshold uncertainty score0.530

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.033
GPT teacher head0.250
Teacher spread0.217 · 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