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The Canadian economy regained strong momentum in 1999 as the U.S. economy remained vigorous, the global economy recovered, and commodity prices moved upwards.

This Report presents the Bank of Canada’s assessment of the trend of inflation in Canada and explains the monetary policy actions deemed necessary to keep inflation within the Bank’s inflation-control target range.

The five articles in this issue present research and analysis by Bank staff covering a variety of topics: the growth of Canadian-dollar-denominated assets in official foreign reserves; the emergence of platform-based digital currencies; methods of forecasting the real price of oil; measures of uncertainty in monetary policy; and the recent performance of the labour market in Canada and the United States.

We develop a model with firm heterogeneity in importing and cross-border shopping among consumers. Exchange-rate appreciations lower the cost of imported goods, but also lead to more cross-border shopping; hence, the net impact on aggregate retail prices and sales is ambiguous.

At the time of the November 2000 Monetary Policy Report, although signs of the anticipated slowing of the U.S. economy were becoming apparent, the momentum of the global economy was considered strong.

In the last half-year, the economic expansion in Canada has become well established, supported by low inflation, highly stimulative monetary conditions, and a strong U.S. economy.

Our study aims to gain insight on financial stability and climate transition risk. We develop a methodological framework that captures the direct effects of a stressful climate transition shock as well as the indirect—or systemic—implications of these direct effects. We apply this framework using data from the Canadian financial system.

This paper studies the implications of model uncertainty for wealth distribution in a tractable general equilibrium model with a borrowing constraint and robustness à la Hansen and Sargent (2008). Households confront model uncertainty about the process driving the return of the risky asset, and they choose robust policies.

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 introduce behavioral learning equilibria (BLE) into DSGE models with boundedly rational agents using simple but optimal first order autoregressive forecasting rules. The Smets-Wouters DSGE model with BLE is estimated and fits well with inflation survey expectations. As a policy application, we show that learning requires a lower degree of interest rate smoothing.