How Important are Financial Shocks for the Canadian Business Cycle
Bibliographic record
Abstract
In this paper, we investigate the importance of financial shocks for the Canadian business cycle employing the financial friction DSGE framework following Bernanke, Gertler, and Gilchrist (1999) with an extension of a small-open economy feature. In particular, we explored the importance of an external finance premium shock and an aggregate net worth shock. In order to identify financial shocks in the model, we utilized financial data in estimating our model. Our variance decomposition results showed that the external finance premium shock to account about 7.5% and the aggregate net worth shock to account about 5.6% of the variance of the business fixed investment in Canada. Also, our historical decomposition results and smoothing of the various financial variables showed that data on corporate leverage ratio to be particularly useful in identifying the financial shocks in the model. Finally, when the financial shocks were present in the model, relative importance of the investment- specific technology shock was substantially subdued that it accounted for only 17% of the variance of the business fixed investment - much lower than the results reported in the former empirical studies.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 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".