A Policy Model for Analyzing Macroprudential and Monetary Policies
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».