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Record W7099560796

participants at the Macroeconomics Brown Bag seminar at the Université de Montréal, the Mid...

2012· article· en· W7099560796 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMathematics
TopicMorphological variations and asymmetry
Canadian institutionsnot available
Fundersnot available
KeywordsShock (circulatory)Order (exchange)Dynamic stochastic general equilibriumNew Keynesian economicsIncentiveProduction (economics)Output gapPhillips curveMonetary policy
DOInot available

Abstract

fetched live from OpenAlex

This Working Paper should not be reported as representing the views of the IMF. The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate. A distinctive feature of market-provided services is that some of them have close substitutes at home. Households may therefore switch between consuming home and market services in response to changes in the real wage—the opportunity cost of working at home—and changes in the price of market services. In order to analyze and quantify the implications of this trade-off for monetary policy, I embed a household sector into an otherwise standard sticky price DSGE model, which I calibrate to the U.S. economy. The results of the model are twofold. At the sectoral level, household production augments the service sector's New Keynesian Phillips curve with a sizable extra component that co-moves negatively with the output gap term, lowering the incentive of service sector firms to change their prices. This mechanism endogenously amplifies the real effects of a monetary shock in that sector, unlike in the nondurable goods sector for which households cannot manufacture substitutes at home. At the aggregate level, household production also implies more sluggish prices and a stronger response of real macroeconomic variables to a monetary shock. Some empirical support for this theory is provided. JEL Classification Numbers:E12, E32, D13.

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)0.003
metaresearch head score (Gemma)0.004
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: none
Teacher disagreement score0.785
Threshold uncertainty score0.718

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.2150.033

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 head0.046
GPT teacher head0.261
Teacher spread0.215 · 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
Published2012
Admission routes1
Has abstractyes

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