Imperfect Exchange Rate Pass-through: Empirical Evidence and Monetary Policy Implications
Bibliographic record
Abstract
We construct a small open economy (SOE) DSGE model interacting with the rest of the world (ROW). We depart from the standard SOE model along several dimensions. Firstly, we nest two different pricing paradigms: local currency pricing (LCP) alongside producer currency pricing (PCP). Second, the production function incorporates capital and intermediate inputs produced domestically and abroad. Finally, international asset markets are incomplete. Using US and Canadian data, we explore the empirical evidence for PCP vs LCP pricing paradigms through a Bayesian estimation likelihood race and a comparison with the second moments of the data. We then examine the implications of these two paradigms for the conduct of monetary policy using optimized Taylor-type inertial interest rate rules with a zero lower bound constraint. The main results are: first, in a likelihood race LCP easily beats PCP and fits reasonably the second moments of the data; second, whereas for the closed economy ROW the price-level rule closely mimics the optimized general inflation-output rule, for the SOE the corresponding result requires a nominal income rule.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".