Exchange rate predictability and monetary fundamentals in a small multi-country panel’, Bank of England, mimeo, conditionally accepted for the
Bibliographic record
Abstract
In this paper a panel of vector error correction models based on a common long-run relationship is utilized to test whether the Euro exchange rates of Canada, Japan and the United States have a long-run link with monetary fundamentals. We use both exchange relationships relative to the full EMU area (with synthetic aggregates for the pre-EMU period) and relative to Germany solely. Compared to existing cointegration frameworks our approach provides more evidence that the aforementioned exchange rates are consistent with a rational expectations-based monetary exchange rate model based on a common long-run relationship, albeit with a longrun impact of relative income that is higher than predicted by the theory. As a next step we analyze the out-of-sample fit of this common long-run exchange rate model relative to naive random walk-based forecasts. These forecasting evaluations indicate that the monetary fundamentals-based common long-run model is superior to both random walk-based forecasts and standard cointegrated VAR model-based forecasts, especially at horizons of 2 to 4 years.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".