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The global economy has been unfolding largely as expected, and prospects for continued robust growth are quite favourable, especially over the near term.

Six months ago, at the time of the last Monetary Policy Report, the global economic and financial environment was volatile and highly uncertain because of the adverse situation in Asia and the fallout from the Russian debt moratorium.

The Canadian economy continues to operate just above its full production capacity, and the near-term outlook for core inflation has moved slightly higher.

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.

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.

Over the past year, Canada’s economy has outperformed the economies of virtually all the other major industrial countries.

Since the last Report, the Canadian economy has advanced broadly in line with expectations.

The global economy has continued to grow at a robust pace since the July Monetary Policy Report Update.

Exporters frequently change their market destinations. This paper introduces a new approach to identifying the drivers of these decisions over time. Analysis of customs data from China and the UK shows most changes are driven by demand rather than supply-related shocks.

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.