Canadian monetary policy analysis using a structural VARMA model
Why this work is in the frame
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Bibliographic record
Abstract
This paper builds a structural VARMA (SVARMA) model for investigating Canadian monetary policy. Despite the support for a VARMA model for monetary policy analysis, the traditional VAR and SVAR models have predominantly been used in the literature mainly due to difficulties associated with the identification and estimation of such a model. Using the scalar component model (SCM) proposed by Athanasopoulos and Vahid (2008a), this paper first identifies a VARMA model and then constructs a SVARMA model for Canadian monetary policy. We included the SVAR model in our study for a comparison purpose. Relative to this model, the impulse responses generated by the SVARMA model appear to be consistent with those predicted by various economic theoretical models, and solves the economic puzzles found commonly in the empirical literature on monetary policy. The successful construction and implementation of the SVARMA model for Canadian monetary policy analysis along with its promising impulse responses, indicate the suitability of this framework for small open economies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it