Bibliographic record
Abstract
How should monetary policy respond to nominal exchange rates? How does this change as economies become increasingly globalised? In this paper, we address these questions for Asia, focusing on structural changes that may influence the optimal policy response to exchange rates. We also summarise some new results based on an analytical model outlined in Devereux and Yetman (2014b) designed to address these issues. We show that sterilised intervention can be a potent tool that offers policymakers an additional degree of freedom in maximising global welfare. We illustrate how the gains to sterilised intervention can be sensitive to various aspects of goods and financial market structure. When financial internationalisation is high, the gains to sterilised intervention fall. And at the limit of perfect financial integration, the gains from sterilised intervention are entirely eliminated. Unsterilised intervention may also have a role to play, and may continue to work even in cases where sterilised intervention is rendered ineffective. Many central banks in Asia have actively used sterilised foreign exchange intervention as a policy tool for smoothing exchange rate movements. This is a policy that appears to have served the region well. But, over time, structural changes in the region, including increased goods market integration, declining exchange rate pass-through and ongoing internationalisation of financial markets are likely to reduce the efficacy of sterilised intervention. More generally, these structural changes may call into question the appropriate role of exchange rates in monetary policy setting in the region.Full publication: Globalisation, Inflation and Monetary Policy in Asia and the Pacific
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".