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Record W2117911140 · doi:10.1257/mac.4.1.22

Effects of Fiscal Stimulus in Structural Models

2010· article· en· W2117911140 on OpenAlexaff
Günter Coenen, Christopher J. Erceg, Charles Freedman, Davide Furceri, Michael Kumhof, René Lalonde, Douglas Laxton, Jesper Lindé, Annabelle Mourougane, Dirk Muir, Susanna Mursula, Carlos A. de Resende, John M. Roberts, Werner Roeger, Stephen Snudden, Mathias Trabandt, Jan in ’t Veld

Bibliographic record

VenueAmerican Economic Journal Macroeconomics · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsBank of CanadaCarleton UniversityQueen's UniversityGovernment of Canada
Fundersnot available
KeywordsStimulus (psychology)Fiscal policyEconomicsMonetary policyFiscal unionFiscal multiplierMacroeconomicsMonetary economicsGovernment spendingPsychologyCognitive psychology

Abstract

fetched live from OpenAlex

The paper subjects seven structural DSGE models, all used heavily by policymaking institutions, to discretionary fiscal stimulus shocks using seven different fiscal instruments, and compares the results to those of two prominent academic DSGE models. There is considerable agreement across models on both the absolute and relative sizes of different types of fiscal multipliers. The size of many multipliers is large, particularly for spending and targeted transfers. Fiscal policy is most effective if it has moderate persistence and if monetary policy is accommodative. Permanently higher spending or deficits imply significantly lower initial multipliers.(JEL E12, E13, E52, E62)

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.015
GPT teacher head0.224
Teacher spread0.209 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations47
Published2010
Admission routes1
Has abstractyes

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