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Record W4392382133 · doi:10.1016/j.jedc.2024.104842

The simple macroeconometrics of the quantity theory and the welfare cost of inflation

2024· article· en· W4392382133 on OpenAlexaff
Kenneth G. Stewart

Bibliographic record

VenueJournal of Economic Dynamics and Control · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsUniversity of Victoria
FundersVictoria University
KeywordsEconomicsUnivariateEconometricsInflation (cosmology)Money supplyWelfareDemand for moneyHomogeneity (statistics)Autoregressive modelMathematical economicsInterest rateMacroeconomicsMathematicsMultivariate statisticsStatistics

Abstract

fetched live from OpenAlex

The quantity theory of money hypothesizes that the price level is determined through the equilibration of money supply and demand. Predicated on this causal structure, a single-equation error correction model decomposes from a larger vector autoregressive system so as to make available bounds tests for a levels relationship that are robust to the univariate integration properties of the variables. This model is estimated using three monetary aggregates and two money demand specifications, for U.S. and U.K. annual data over the past century and quarterly post-WWII data. The classic homogeneity propositions of the quantity theory are testable, and are found to be most compatible with U.S. annual M2 using log-log money demand with structural change permitted. Nevertheless, the resulting welfare costs are similar to those yielded by the U.K. annual data, being less than one percent of GDP at interest rates experienced during the past century.

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 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)0.004
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score0.316

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.220
Teacher spread0.206 · 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 teacher head, not a consensus.

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

Citations2
Published2024
Admission routes1
Has abstractyes

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