The Impact of U.S. Monetary Policy Normalization on Capital Flows to Emerging-Market Economies
Bibliographic record
Abstract
The Federal Reserve’s path for withdrawal of monetary stimulus and eventually increasing interest rates could have substantial repercussions for capital flows to emerging-market economies (EMEs). This paper examines the potential impact of U.S. monetary policy normalization on portfolio flows to major EMEs by using a vector autoregressive model that explicitly accounts for market expectations of future monetary policy. The “policy normalization shock” is defined as a shock that increases both the yield spread of U.S. long-term bonds and monetary policy expectations while leaving the policy rate per se unchanged. Results indicate that the impact of this shock on portfolio flows as a share of GDP is expected to be economically small. The estimated impact is closely in line with that seen during the end-May to August 2013 episode in response to a comparable rise in the yield spread of U.S. long-term bonds. However, as the events during the summer of 2013 have shown, relatively small changes in portfolio flows can be associated with significant financial turmoil in EMEs. Further, there is also a strong association between the countries that are identified by our model as being the most affected and the ones that saw greater outflows of portfolio capital over May to September 2013.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".