MétaCan
Menu
Back to cohort
Record W4380367159 · doi:10.3386/w31337

The Empirical Distribution of Firm Dynamics and Its Macro Implications

2023· report· en· W4380367159 on OpenAlexafffund
Nir Jaimovich, Stephen Terry, Nicolas Vincent

Bibliographic record

VenueNational Bureau of Economic Research · 2023
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsHEC Montréal
FundersSocial Sciences and Humanities Research Council of CanadaHEC MontréalFondation HEC
KeywordsMacroDynamics (music)Distribution (mathematics)EconometricsMathematicsEconomicsComputer scienceSociologyMathematical analysis

Abstract

fetched live from OpenAlex

Heterogeneous firm models are ubiquitous in modern macroeconomics.We revisit a central feature of these models: the idiosyncratic shock process faced by firms.Using a large representative firm-level dataset, we document nonparametrically that the common assumption, a Gaussian AR(1) shock process, is at odds in important ways with observed fat-tailed firm dynamics.We embed these findings within a standard quantitative general equilibrium heterogeneous firm dynamics model and show that the nature of firm-level shocks has a sizable quantitative effect on the economy's responsiveness to aggregate shifts.

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)0.006
metaresearch head score (Gemma)0.003
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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.934
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.645
GPT teacher head0.536
Teacher spread0.108 · 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 designTheoretical or conceptual
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

Citations9
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueNational Bureau of Economic ResearchSame topicFirm Innovation and GrowthFrench-language works237,207