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Record W2598802930

Vilka samband råder mellan real växelkurs och olika sektorer av svensk utrikeshandel

2017· article· sv· W2598802930 on OpenAlexaboutno aff
Björn Olsson

Bibliographic record

VenueLund University Publications Student Papers (Lund University) · 2017
Typearticle
Languagesv
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsExchange rateBalance of tradeDistributed lagQuarter (Canadian coin)Inflation (cosmology)Real gross domestic productForeign direct investmentEffective exchange rateTerms of tradeInternational economicsInvestment (military)Monetary economicsEconometricsMacroeconomicsGeography
DOInot available

Abstract

fetched live from OpenAlex

This paper seeks to investigate the effects of the real exchange rate on the Swedish trade balance. By investigating for the elasticities of demand from the real exchange rate on Swedish exports and imports this paper seeks to present a more detailed and specific estimate than previous studies on the trade balance. An Autoregressive dynamic lag model (ARDL) is used to conduct regressions, using lags of the dependent and independent variables as regressors. The data involves quarterly statistics for Swedish imports and exports of services, produced goods and commodities, Sweden’s and foreign GDP, Sweden’s net international investment position, Brent crude oil price and Swedish real exchange rate measured relative to the country’s main trading partners. The data is from the first quarter of 1995 to the second quarter of 2016. This paper finds some evidence of a statistically significant real exchange rate effect on Swedish foreign trade, especially on the import side. However, the estimated results also suggest that the real exchange rate only has a small effect on export. This suggest that other factors, such as GDP affect Swedish trade to a higher degree. The policy implication is that the importance of the real exchange rate on the trade balance should not be overestimated, but rather seen as one of several minor factors affecting trade, where domestic and foreign GDP being the major factors.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.894
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0050.001
Scholarly communication0.0010.003
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.050
GPT teacher head0.231
Teacher spread0.181 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2017
Admission routes1
Has abstractyes

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