What Drives Exchange Rates? New Evidence from a Panel of U.S. Dollar Bilateral Exchange Rates
Bibliographic record
Abstract
We use a novel approach to identify economic developments that drive exchange rates in the long run. Using a panel of six quarterly U.S. bilateral real exchange rates – Australia, Canada, the euro, Japan, New Zealand and the United Kingdom – over the 1980-2007 period, a dynamic factor model points to two common factors. The first factor is driven by U.S. shocks, and cointegration analysis points to a long-run statistical relationship with the U.S. debt-to-GDP ratio, relative to all other countries in our sample. The second common factor is driven by commodity prices. Incorporating these relationships directly into a state-space model, we find highly significant coefficients. Then, we decompose the historical variation of each exchange rate into U.S. shocks, commodities, and a domestic component. We find a strong role for economic fundamentals: Changes in the two common factors, which are driven by the (relative) U.S. debt-to-GDP ratio and commodity prices, can explain between 36 and 96 per cent of individual countries’ exchange rates in our panel.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".