Globalisation, Pass-through and the Optimal Policy Response to Exchange Rates
Bibliographic record
Abstract
In this paper we examine how monetary policy should respond to nominal exchange rates in a New Keynesian open economy model that allows for a non-trivial role for sterilised intervention.The paper develops the argument against the backdrop of the evolving policy-making environment of Asian economies.Sterilised intervention can be a potent tool that offers policymakers an additional degree of freedom in maximising global welfare.We show that the gains to sterilised intervention are greater when goods market integration is low and exchange rate pass-through is high.However, increased financial internationalisation reduces the effectiveness of sterilised intervention, as the international policy trilemma becomes more relevant.Unsterilised intervention may also have a role to play, although the potential welfare gains from this are generally smaller.Most central banks in Asia have actively used sterilised foreign exchange intervention as a policy tool to smooth exchange rates.But, over time, declining exchange rate pass-through and the increasing international integration of financial and goods markets will tend to reduce the efficacy of sterilised intervention.Given the limited effectiveness of unsterilised intervention, our model implies that the role of exchange rate movements in the optimal setting of monetary policy in Asia is decreasing.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".