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The two key components of the Bank's monetary policy framework are an "anchor," the inflation target, and a "float," the flexible exchange rate. Living by the ocean, you know better than I that a good mooring is one that keeps a boat in place, yet allows some give and take for the wind and the tide.

We show that in a heterogeneous economy, optimal policy after cost-push shocks raises short-term rates to curb inflation while lowering long-term rates to support indebted households, speeding investment and output recovery while increasing consumption inequality.

Worst-case analysis is used among financial regulators in the wake of the recent financial crisis to gauge the tail risk. We provide insight into worst-case analysis and provide guidance on how to estimate it. We derive the bias for the non-parametric heavy-tailed order statistics and contrast it with the semi-parametric extreme value theory (EVT) approach.

Optimal coordination of monetary and macroprudential policies implies higher risk weights on (safe) bonds any time that banks are required to hold additional capital buffers. Coordination also implies a somewhat tighter monetary-policy stance whenever such capital buffers are released.

As we celebrate the 400th anniversary of the founding of Quebec, I thought it would be fitting for me, as a central banker, to take a look at how money has evolved over part of the past four centuries.

A year ago, in early 1997, prospects for global economic growth were very promising. World economic activity had strengthened and was expected to accelerate further, with the benefit of low inflation, reduced fiscal imbalances, and stable or declining interest rates. In Canada too, output and employment growth had picked up.

This paper analyzes the implications of the global financial cycle for conventional and unconventional monetary policies and macroprudential policy in small, open economies such as Canada. The paper starts by summarizing recent work on financial cycles and their growing correlation across borders.

Using the quantum Monte Carlo algorithm, we study whether quantum computing can improve the run time of economic applications and challenges in doing so. We apply the algorithm to two models: a stress testing bank model and a DSGE model solved with deep learning. We also present innovations in the algorithm and benchmark it to classical Monte Carlo.

We conduct surveys to study how consumer spending responds to higher inflation expectations. Most respondents spend the same, sticking to fixed budget plans or not considering inflation for spending decisions. About 20% decrease spending because they feel poorer and cut spending to invest in inflation-proof assets. Very few increase spending.