A Policy Model for Analyzing Macroprudential and Monetary Policies
Bibliographic record
Abstract
The recent global financial crisis was a reminder that economic and financial stability are inextricably linked. To that end, there has been significant effort in policy and academic circles to incorporate real-financial linkages and macroprudential policies into existing macroeconomic models. Our policy model described below contributes to this literature. The objective is to incorporate all three balance sheets of households, firms and banks within a single unified framework. This would allow us to analyze key policy questions such as assessing the effects of a house price decline on banks’ capital positions, and its spillover effects on the business sector and the broader economy. The model can also be used to investigate the appropriate mix of policies (i.e. monetary policy, LTV and bank capital regulations, and fiscal policy) to simultaneously tackle issues related to macroeconomic and financial stability. We build a medium scale, small open economy DSGE model with real, nominal and financial frictions to analyze the effects of various shocks and policies on the Canadian economy. The model features non-trivial interactions between the balance sheet positions of households, firms and banks. Savings of patient households are partly intermediated through banks, which help finance the purchases of capital by entrepreneurs and purchases of housing by impatient households. Financial frictions in the form of monitoring costs generate spreads in both the funding and the lending rates of banks, which in equilibrium depend on the balance sheet positions of banks and borrowers respectively. Regulations on bank capital requirements and loan-to-value (LTV) ratios are modeled so that they feed into these spreads, and do not necessarily bind every period. The effects of asset prices on balance sheets of banks and borrowers, and the presence of monitoring costs, generate significant amplification in the system and spillovers across different sectors. For example, an increase in house prices leads to an improvement in the household balance sheets, which reduces banks’ monitoring costs for mortgage loans and strengthens bank balance sheets. This in turn leads to better funding conditions for banks which are then able to lend to entrepreneurs as well as households at cheaper rates. The model is calibrated to match the dynamics in Canadian macroeconomic and financial data, and can be simulated to explore various policy scenarios relevant for the Canadian economy. Macroprudential policies are better suited to counteract financial stability issues arising from household indebtedness, relative to monetary policy. Within macroprudential polices, LTV policy is more targeted towards dealing with household debt and is more effective and less costly in terms of output impact relative to bank capital regulations which are more broad-based.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".