Valuation Effects and the Dynamics of Net External Assets
Bibliographic record
Abstract
The traditional current account can be an inaccurate measure of the change in the net foreign asset (NFA) position. Using gross asset and liability positions at the country level, a number of 'valuation effects' have been identified which contribute to changes in NFA but do not enter the reported current account. This paper uses new developments in the analysis of portfolio allocation in general equilibrium to investigate valuation effects in a two-country model. The model can be used to analyze both qualitatively and quantitatively the role of valuation effects. Broadly speaking, the valuation effects in the model correspond to those in the data, and have the effect of enhancing cross country risk sharing. But there is a key distinction between "unanticipated" and "anticipated" valuation effects. Unanticipated effects can be large, dominating the movement in NFA, but anticipated effects arise only at higher orders of approximation and are small for reasonable parameterisations. The paper also analyses the determinants of international portfolio positions, and their role in generating valuation effects from asset price and terms of trade changes.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".