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Record W2890706578 · doi:10.3386/w11043

State-Dependent or Time-Dependent Pricing: Does it Matter for Recent U.S. Inflation?

2005· preprint· en· W2890706578 on OpenAlex

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

VenueNational Bureau of Economic Research · 2005
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsBank of Canada
Fundersnot available
KeywordsInflation (cosmology)EconomicsKeynesian economicsState (computer science)Monetary economicsEconometricsMathematical economicsPhysicsComputer scienceTheoretical physicsAlgorithm

Abstract

fetched live from OpenAlex

Inflation equals the product of two terms: an extensive margin (the fraction of items with price changes) and an intensive margin (the average size of those price changes). The variance of inflation over time can be decomposed into contributions from each margin. The extensive margin figures importantly in many state-dependent pricing models, whereas the intensive margin is the sole source of inflation changes in staggered time-dependent pricing models. We use micro data collected by the U.S. Bureau of Labor Statistics to decompose the variance of consumer price inflation from 1988 through 2003. We find that around 95% of the variance of monthly inflation stems from fluctuations in the average size of price changes, i.e., the intensive margin. When we calibrate a prominent statedependent pricing model to match this empirical variance decomposition, the model's shock responses are very close to those in time-dependent pricing models.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.749
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.007

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 head0.315
GPT teacher head0.437
Teacher spread0.123 · 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