Financial Frictions and Macroeconomy During Financial Crises: A Bayesian DSGE Assessment
Bibliographic record
Abstract
The recent global financial crisis and the Eurozone sovereign default have rekindled the debate on the interactions between the real sector and the financial sphere. The present paper provides an assessment of the role of financial frictions on business cycles in Canada, the Euro Area, the U.K., and the U.S. during these recent financial crises using an extension of the DSGE methodology described by Merola (2015). The main goal is to examine whether and the extent to which those crises enhanced the contribution of financial frictions in driving macroeconomic fluctuations. The models’ properties are examined with posteriors distributions, variance decomposition, and historical decomposition. Posteriors distributions show that the role of real shocks in driving macroeconomic fluctuations decrease with the incorporation of financial frictions in the core DSGE model. Variance decomposition shows that financial frictions and financial shocks affect the business cycle through investment. The empirical estimates also suggest that the contribution of financial frictions and financial shocks in driving investment increases during the global financial crisis.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".