Vilka samband råder mellan real växelkurs och olika sektorer av svensk utrikeshandel
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".