Behavioural equilibrium exchange rate estimates and implied exchange rate adjustments for ten countries
Bibliographic record
Abstract
In this paper we estimate the behaviour equilibrium exchange rates (BEERs) of Clark and MacDonald (1999) for the effective exchange rates of ten industrialised and emerging market economies that rank within the top 15 contributory economies to global imbalances. The sample period is 1988, quarter 1 to 2006 quarter 1. The conditioning variables used in the estimation of the BEER are: net exports as a proportion of GDP, a real interest differential, a terms of trade differential and GDP per capita differential. The ‘foreign’ magnitudes in the differentials were constructed using the trade weights used to construct the effective exchange rates. Using both single country and panel econometric methods, plausible BEER estimates were reported. These estimates were then used to back out the required exchange rate adjustments necessary to fulfil the three scenarios of Williamson (2006). The ball park currency adjustments required are in the range of 27.3 to 46.6 per cent devaluations for the Chinese renminbi, 5 to 11 per cent for the US dollar, approximately 6 per cent for the Japanese yen and no adjustment for the euro or Sterling.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".