Understanding the Volatility of the Canadian Exchange Rate
Bibliographic record
Abstract
In this Commentary, we document the nature of the Bank of Canada’s current monetary policy regime by focusing on the following questions: what are the historical determinants of the Canadian–US dollar nominal exchange rate, and can they be used in real-time forecasting applications? We find that the current real exchange rate is more useful than commodity prices for forecasting changes in the nominal exchange rate. In fact, short-run forecasts based on the real exchange rate are as good as random-walk forecasts according to which the future exchange rate is expected to be the same as today’s. Strikingly, medium- and long-run forecasts based on the real exchange rate are superior to random-walk forecasts. We argue that these findings reflect the fact Bank of Canada and the U.S. Federal Reserve System follow similar inflation-targeting regimes and neither actively manages exchange rates. A fundamental question is whether Canadian policymakers are satisfied with the current inflationtargeting regime. A cost of the current regime is that it allows for very volatile nominal and real exchange rates. A benefit is that consumers and firms can avoid many of the changes in prices and wages that would be required if the nominal exchange rate did not adjust in a flexible manner. In this Commentary, we take no stand on the merits of the current regime. Instead, we highlight the tradeoffs that policymakers face. Evaluating the costs and benefits of those tradeoffs should play an important role in the process leading to the Bank of Canada’s next five-year agreement with the government.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.013 | 0.013 |
| 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".