Sudden Stops and Optimal Foreign Exchange Intervention
Bibliographic record
Abstract
This paper shows how foreign exchange intervention can be used to avoid a sudden stop in capital flows in a small open emerging market economy. The model is based around the concept of an under-borrowing equilibrium defined by Schmitt-Grohe and Uribe (2020). With a low elasticity of substitution between traded and non-traded goods, real exchange rate depreciation may generate a precipitous drop in aggregate demand and a tightening of borrowing constraints, leading to an equilibrium with an inefficiently low level of borrowing. The central bank can preempt this deleveraging cycle through foreign exchange intervention. Intervention is effective due to frictions in private international financial intermediation. Reserve accumulation has ex ante benefits by reducing the risk of a sudden stop, while intervention has ex-post benefits by limiting inefficient deleveraging. But intervention itself faces constraints. When the central bank's stock of reserves is low, even foreign exchange intervention cannot prevent a sudden stop.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".