Bibliographic record
Abstract
For policymakers, thinking about best practice monetary policy means thinking about uncertainty. Open economy monetary policymakers face an additional source of uncertainty – exchange rate dynamics. This paper identifies policy rules robust to the open economy inflation targeters face in practice. For Knight (1921), uncertainty differs from risk because the policymaker does not know the nature of the uncertainty and is unable to form a probability distribution or risk statement, over different possible models. Hansen and Sargent (2004) apply Knight’s (1921) philosophy to the linear-quadratic control framework, recognizing that policy-makers work with models which are approximations to some true, unknown model and seek a rule that is robust to models close to the policymaker’s best approximation. While there exist some open economy robust control policy experiments (Leitemo and Söderström (2004) obtain analytical robust control solutions for a purely forward-looking new Keynesian model) the majority of the literature focuses on the closed economy. This paper calibrates a single open economy model to capture the key open economy dynamics for Australia, Canada and New Zealand, three of the earliest inflation targeters that form a useful dataset for identifying robust monetary policy rules in practice. Robust policies are found to respond more aggressively to not only inflation and the output gap, but also the exchange rate and its associated shock. This result generalizes to the context of a flexible inflation targeting central bank that cares about the volatility of the real exchange rate. However, when the central bank places only a small weight on interest rate smoothing and fears misspecification in only exchange rate determination, a more aggressive response to the lag of the exchange rate is not warranted. It is shown that the benefits of an exchange rate channel far outweigh the concomitant costs of uncertain exchange rate determination.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".