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Record W2118999431 · doi:10.1017/s1365100505040307

SEMI-NONPARAMETRIC ESTIMATES OF THE DEMAND FOR MONEY IN THE UNITED STATES

2005· article· en· W2118999431 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

VenueMacroeconomic Dynamics · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEconomicsContext (archaeology)Nonparametric statisticsEconometricsAggregate (composite)Demand for moneySimple (philosophy)Substitution (logic)Aggregate demandMonetary policySet (abstract data type)Perspective (graphical)Ideal (ethics)MaximizationMathematical economicsUtility maximizationMicroeconomicsMathematicsMacroeconomicsComputer science

Abstract

fetched live from OpenAlex

This paper focuses on the demand for money in the United States in the context of two globally flexible functional forms—the Fourier and the asymptotically ideal model (AIM)—estimated subject to full regularity, using methods suggested over 20 years ago. We provide a comparison in terms of violations of the regularity conditions for consumer maximization and in terms of output in the form of a full set of elasticities. We also provide a policy perspective, using (for the first time) parameter estimates that are consistent with global regularity, in that a very strong case can be made for abandoning the simple-sum approach to monetary aggregation, on the basis of the low elasticities of substitution among the components of the popular M2 aggregate of money.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.256
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.029
GPT teacher head0.236
Teacher spread0.207 · 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