The impact of macroprudential policies on the transmission of shocks across financially integrated countries
Bibliographic record
Abstract
Abstract We study the implications of macroprudential policies across countries on the transmission of shocks when international investment activities are allowed. In a two‐country dynamic stochastic general equilibrium (DSGE) model in which international investors are borrowing constrained and pledge international assets, we introduce a time‐varying loan‐to‐value (LTV) ratio that adjusts to the variation of three different financial vulnerability indicators. We examine the effect of these policies on negative productivity and borrowing capacity shocks. Although time‐varying LTV ratios reduce the international propagation of the productivity shock, their response to the shock depends on the financial vulnerability indicator with which the LTV ratio changes. With a productivity shock, the adjustment of the LTV ratio to the deviation of credit or asset price helps to reverse the negative impact of the shock. With a financial shock, LTV ratios varying with a deviation of credit‐to‐GDP ratio or aggregate credit can mitigate the impact of a negative financial shock. Adjustment of the LTV ratios reduces the fluctuation of international investors' balance sheets, investment, and productivity. We find that countries improve their welfare when time‐varying LTV ratios are in place. The magnitude of the welfare gain differs with both the financial vulnerability indicator and the shock.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".