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9094 Results

The Bank of Canada’s “Horse Race” of Alternative Monetary Policy Frameworks: Some Interim Results from Model Simulations

Staff Discussion Paper 2021-13 José Dorich, Rhys R. Mendes, Yang Zhang
Bank of Canada staff are running a “horse race” of alternative monetary policy frameworks in the lead-up to 2021 renewal of the Bank’s monetary policy framework. This paper summarizes some interim results of model simulations from their research.

Centralizing Over-the-Counter Markets?

Staff Working Paper 2021-39 Jason Allen, Milena Wittwer
Would a shift in trading in fixed-income markets—from over the counter (bilateral trading) to a centralized electronic platform—improve welfare? We use trade-level data on the secondary market for Government of Canada debt to answer this question.

Michael Irwin

Michael Irwin is a Senior Economist in the Financial Stability Department at the Bank of Canada.

The uneven economic consequences of COVID 19: A structural analysis

Staff Analytical Note 2021-17 Martin Kuncl, Austin McWhirter, Alexander Ueberfeldt
Using a structural model, we study the economic consequences of the COVID-19 shock. The uneven consequences, such as higher unemployment among young households, amplify the negative implications for the macroeconomy, household vulnerabilities and consumption inequality. Government support programs have stimulated the economy and lowered inequality and medium-term vulnerabilities.

Monetary Policy and the Persistent Aggregate Effects of Wealth Redistribution

Staff Working Paper 2021-38 Martin Kuncl, Alexander Ueberfeldt
Monetary policy in the presence of nominal debt and labour supply heterogeneity creates a policy trade-off: a short-term economic stimulus leads to persistently reduced output over the medium term. Price-level targeting weakens this trade-off and is better able to stabilize inflation and output than inflation targeting.

Estimating Large-Dimensional Connectedness Tables: The Great Moderation Through the Lens of Sectoral Spillovers

Staff Working Paper 2021-37 Felix Brunner, Ruben Hipp
Understanding the size of sectoral links is crucial to predicting the impact of a crisis on the whole economy. We show that statistical learning techniques substantially outperform traditional estimation techniques when measuring large networks of these links.
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