Bibliographic record
Abstract
Countries such as advanced systemic economies (ASEs) and systemic middle-income countries (SMICs), considering macroprudential policy coordination, must ensure that their financial cycles are sufficiently synchronized. However, differences in the features and significance of financial cycles between ASEs and SMICs pose challenges in determining the extent of their synchronization. Accordingly, this study assesses whether a common financial cycle exists between these types of economies. The point of departure for this analysis is to examine the characteristics of the common financial cycle. To this end, this study employs data on capital flows, credit, house prices, share prices, and policy rates, utilizing the Markov switching dynamic regression model and the dynamic factor model to identify and analyze the cycle. The findings reveal strong evidence of a significant financial cycle, which explains 83% of the total variation across countries. This cycle is characterized by longer durations compared to domestic financial cycles and occurs less frequently than domestic cycles. Moreover, it exhibits high persistence in its contractionary and expansionary phases, with greater volatility in the contractionary phase. Based on these findings, it is recommended that ASEs and SMICs consider establishing a supranational prudential authority to coordinate and oversee macroprudential policy on behalf of the majority. Such an entity should play a proactive role, particularly during contractionary phases, to mitigate systemic risks and enhance financial stability across these interconnected economies.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".