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Over the last six months, most countries have continued to register strong economic growth.

Household debt can be an important source of vulnerability to the financial system. This technical report describes the Household Risk Assessment Model (HRAM) that has been developed at the Bank of Canada to stress test household balance sheets at the individual level.

Since the May Report, the international economic environment has continued to improve. Economic activity abroad grew faster than expected, while inflation in the major economies remained subdued.

The COVID-19 pandemic has caused an atypical recession in which some sectors of the economy boomed and others collapsed. This required a unique fiscal policy reaction to both support firms and stimulate activity in sectors with slack. Was fiscal policy able to get where it was needed? Mostly, yes.

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.

This is the second in a series of semi-annual reports designed to increase the transparency and understanding of Canadian monetary policy.

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.

Two major issues dominate the analysis and policy discussion in this Monetary Policy Report: the nature and extent of the global economic slowdown that began late last year and the consequences of the terrorist attacks in the United States.

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

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.