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Record W4205279678 · doi:10.1139/cjfr-2021-0326

Synchronized movement between US lumber futures and southern pine sawtimber prices and COVID-19 impacts

2022· article· en· W4205279678 on OpenAlexvenueno aff
Jianbang Gan, Nana Tian, Junyeong Choi, Matthew H. Pelkki

Bibliographic record

VenueCanadian Journal of Forest Research · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
FundersU.S. Department of Agriculture
KeywordsEconomicsFutures contractCoronavirus disease 2019 (COVID-19)ShakerEconometricsMonetary economicsFinancial economics

Abstract

fetched live from OpenAlex

We analyzed the synchronized movements of lumber futures and southern pine sawtimber stumpage prices in the United States since 2011 and their response to COVID-19 events using wavelet analysis and event study. We found that the sawtimber and lumber prices have followed complex comovement patterns in the time–frequency domain and both reacted to COVID-19 events with a higher response intensity of the lumber price. Although they reacted differently to the early COVID-19 episodes and vaccine news, the sawtimber and lumber prices responded similarly to the COVID-19 pandemic declarations by the World Health Organization and US president, the US Food and Drug Administration panel’s recommendation of the first COVID-19 vaccine, and economic stimulus legislation. The patterns of synchronized movements between the sawtimber and lumber prices varied with time and frequency, but their comovement at low frequencies (>64 weeks) has strengthened since 2014 and been led by the lumber futures price; COVID-19 episodes have not changed this trend. The different magnitude of response of the two prices to the COVID-19-related events, as well as the long-term dominance of the lumber price in the comovement, reveals asymmetric price negotiation power and benefit distributions among the agents of the lumber value chain.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.000

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 head0.033
GPT teacher head0.316
Teacher spread0.283 · 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.

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

Citations14
Published2022
Admission routes1
Has abstractyes

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