Impact of exchange rate volatility on international trade: Case of USA and Canada
Bibliographic record
Abstract
Purpose: The aim of this study is to investigate the impact that exchange rate volatility has on international trade flows including here exports and imports. Design/methodology/approach: This study is based on quarterly data from 2000-2018 making 224 observations in total. To measure the relationship between the chosen variables, it was used VAR-Vector Autoregressive Model. One of the main advantages of this model is traced back at the fact that it allows for dynamic relationship specification. Given that we are dealing with financial and macroeconomic variables, the role of each variable cannot be expected to be immediately monitored. On the contrary, it could be expected that it takes time for the interrelationships to be obvious and manifested. All this justifies the use of VAR. In total, two equations each with three independent variables are used to answer to the research question. Regressors are selected after a deliberate literature review and they are: price level, GDP, exchange rate and its volatility. Findings: The results suggest that there exists indeed an impact of exchange rate volatility on international trade among the US and Canada. This relationship seems to be changing among months and at different levels of significance. The final findings indicate a positive long-run relationship between exchange rate volatility and exports. These results are in line with the findings that other researchers have concluded in their studies. Regarding the imports, there exists a long-run relationship, but its impact differs in different periods. Research limitations/implications: One of the limitations and also a recommendation for improvement, is the number of explanatory variables. This came as a result of some lack of data for the included period. Originality/value: This topic is not only of great importance to policy maker, but it is also an added value to the current literature on the matter as it provides a thorough up-to-date analysis. It also draws on a sample of considerable size, thus providing consistency.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".