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Record W2088475407 · doi:10.1139/x10-150

Effects of exchange rate volatility on export volume and prices of forest products

2010· article· en· W2088475407 on OpenAlexvenueno aff
Sijia Zhang, Joseph Buongiorno

Bibliographic record

VenueCanadian Journal of Forest Research · 2010
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsExchange rateVolatility (finance)EconomicsAutoregressive conditional heteroskedasticityEconometricsPoolingAutoregressive modelMonetary economics

Abstract

fetched live from OpenAlex

The relative value of currencies varies considerably over time. These fluctuations bring uncertainty to international traders. As a result, the volatility in exchange rate movements may influence the volume and the price of traded commodities. The volatility of exchange rates was measured by the variance of residuals in a GARCH(1,1) model of the exchange rate. We estimated the effect of this exchange rate volatility on export quantity and price with autoregressive distributed lag models based on monthly data of US exports and prices to 14 countries for eight commodity groups. The most general and statistically significant results were obtained by pooling the time series data across destination countries and products. They suggested that an increase in exchange rate variability of 1% led to a short-run decrease in export quantity of 0.3%–0.4% and to a short-run decrease in export price of 0.1%. Both the quantity and the price effect faded away over time. The effects were less systematic and statistically significant for specific export destinations or individual products. Thus, in contrast with exchange rate level, exchange rate volatility may not be a major policy issue for US forest product exports.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.070
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.265
Teacher spread0.228 · 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 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

Citations13
Published2010
Admission routes1
Has abstractyes

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