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Record W7100274653

The views expressed here are those of the authors and do not reflect those of the Bank of Canada. We thank George

2010· article· en· W7100274653 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsAnticipation (artificial intelligence)Monetary policyNew Keynesian economicsOrder (exchange)Business cycleIncentiveCentral bankMonetary theory
DOInot available

Abstract

fetched live from OpenAlex

A growing consensus in New Keynesian macroeconomics is that nominal cost rigidities, rather than countercyclical markups account for the bulk of the real effects of monetary policy shocks. We revisit these conclusions using theory and direct evidence on quantities. We study an economy with nominal rigidities in which goods are storable. Theory predicts that if costs of production are sticky and markups do not vary much in response to, say, expansionary monetary policy, firms react by excessively accumulating inventories in anticipation of future cost increases. In contrast, in the data inventories are fairly constant over the cycle and in response to changes in monetary policy. We show that markups must decline sufficiently in times of a monetary expansion in order to reduce firms ’ incentive to hold inventories and thus bring the model’s inventory predictions in line with the data. Versions of the model consistent with the dynamics of inventories in the data imply that countercyclical markups account for a sizable (50-80%) fraction of the response of real variables to monetary 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.

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)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.816
Threshold uncertainty score0.755

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.006
Science and technology studies0.0040.001
Scholarly communication0.0060.003
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.2260.048

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 head0.076
GPT teacher head0.247
Teacher spread0.171 · 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.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2010
Admission routes1
Has abstractyes

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