Exchange Rate Stability and Monetary Policy in Canada: A Markov-Switching DSGE Approach
Bibliographic record
Abstract
This study estimates the monetary policy rule in Canada using a Markov-switching dynamic stochastic general equilibrium (DSGE) model. The interest rate policy rule is estimated based on two blocks in which the underlying regimes are different: the response of the interest rate to the changes in the inflation rate and output gap (i.e., traditional Taylor rule block in a closed economy) and the response to the exchange rate change. Each block is independently estimated by dividing it into two regimes with strong and weak interest rate responses. According to the estimation results, the period estimated to be a regime that strongly (weakly) responds to the inflation rate and output gap is simultaneously a period of a regime that weakly (strongly) responds to the exchange rate change. As interest rates have different purposes in each block, a trade-off between the two goals is in line with the following trilemma: exchange rate stability and monetary autonomy for internal balance are not simultaneously achieved in an open capital market. Considering that the above trilemma is binding, our counterfactual experiments show that a monetary policy that does not responds strongly to exchange rate changes performs better in terms of welfare in general but this is not always the case for certain types of shocks.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".