Bibliographic record
Abstract
As of mid-November, the US dollar has become overvalued by about 11 percent. The prospect of fiscal stimulus and associated interest rate increases under the new US administration risks still further increases in the dollar. An even stronger dollar would widen the path of growing trade deficits already in the pipeline. As President-elect Donald Trump has attributed trade deficits largely to past trade agreement “disasters, ” there is a corresponding risk of escalating trade policy conflict, in a perverse dynamic reminiscent of the initial years of Reaganomics. In October 2016, the base month of this new set of fundamental equilibrium exchange rate (FEER) estimates, the US dollar was overvalued by 8 percent, about the same amount as identified in the three previous issues in this series. The real effective exchange rate (REER) of the dollar in October was 17 percent above its level in mid-2014. Given the two-year lag from the exchange rate signal to the trade outcome, the US current account deficit is on track to widen from 2.7 percent of GDP this year to nearly 4 percent by 2021. The new estimates, all based on October exchange rates, again find a modest undervaluation of the yen (by 3 percent) but no misalignment of the euro and Chinese renminbi. The Korean won is undervalued by 6 percent. Cases of significant overvaluation besides that of the United States include Argentina (by about 7 percent), Turkey (by about 9 percent), Australia (by about 6 percent), and New Zealand (by about 4 percent). A familiar list of smaller economies with significantly undervalued currencies once again shows undervaluation in Singapore and Taiwan (by 26 to 27 percent), and Sweden and Switzerland (by 5 to 7 percent).
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.011 |
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".