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Record W2127795025 · doi:10.1007/s11698-018-0173-5

From Boom to Bust: A Typology of Real Commodity Prices in the Long Run

2013· article· en· W2127795025 on OpenAlexafffund
David S. Jacks

Bibliographic record

VenueCliometrica · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsSimon Fraser University
FundersSocial Sciences and Humanities Research Council of CanadaNational Agricultural Statistics ServiceUniversity of PennsylvaniaUniversity of New South WalesUniversity of CambridgeU.S. Geological SurveyJohns Hopkins UniversityHarvard University
KeywordsBustCommodityBoomEconomicsContangoVolatility (finance)Monetary economicsShort runFinancial economicsEconomyFutures contractMarket economyEngineering

Abstract

fetched live from OpenAlex

This paper considers the evidence on real commodity prices from 1900 to 2015 for 40 commodities, representing 8.72 trillion US dollars of production in 2011. In doing so, it suggests and documents a comprehensive typology of real commodity prices, comprising long-run trends, medium-run cycles, and short-run boom/bust episodes. The main findings can be summarized as follows: (1) real commodity prices have been on the rise—albeit modestly—from 1950; (2) there is a pattern—in both past and present—of commodity price cycles, entailing large and long-lived deviations from underlying trends; (3) these commodity price cycles are themselves punctuated by boom/bust episodes which are historically pervasive.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.006
Science and technology studies0.0010.002
Scholarly communication0.0020.004
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.040
GPT teacher head0.262
Teacher spread0.222 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations11
Published2013
Admission routes2
Has abstractyes

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