Federal Reserve Bank of Philadelphia or the Federal Reserve SystemEXCHANGE RATES AND MONETARY POLICY REGIMES IN CANADA AND THE U.S.
Bibliographic record
Abstract
ABSTRACT This paper examines monetary regime switching in Canada and the United States and the implications of regime switching for exchange rates and key nominal and real macroeconomic aggregates for the two countries. Evidence of Markov regime switching in the process governing monetary base growth and in the bilateral exchange rate between the two countries is presented. Given the evidence, a two-country general equilibrium monetary model is constructed to account for observed properties of the U.S.-Canadian dollar exchange rate and for measured effects of monetary policy on key variables. Agents in the model face a monetary policy process with regime switching and form beliefs about regimes and money growth using observations and Bayesian learning. With the driving process for money growth rates parameterized using estimates from U.S. and Canadian data, quantitative implications of the model for behaviors of exchange rates and other key variables are examined. The findings are that inclusion of learning by agents contributes somewhat to the model’s ability to account for persistence in effects of money shocks on variables, provided that the shocks themselves are persistent; inclusion of learning contributes little in accounting for business cycle fluctuations and exchange rate variability; inclusion of a nonlinear driving process for money growth rates is important for the model to account for long swings in exchanges rates; inclusion of learning adds only slightly to the ability of the model to account for long
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.098 | 0.018 |
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".