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There have been a number of significant economic and financial developments since the time of the July Monetary Policy Report Update.

In our analysis of the US productivity slowdown in the 1970s and 2000s, we find that a significant portion of this deceleration can be attributed to a lack of improvement in allocative efficiency across sectors. Our analysis further identifies increased sector-level volatility as a major contributor to this lack of improvement in allocative efficiency.

How do banks' interconnections in the euro area contribute to the vulnerability of the banking system? We study both the direct interconnections (banks lend to each other) and the indirect interconnections (banks are exposed to similar sectors of the economy). These complex linkages make the banking system more vulnerable to contagion risks.

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

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

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.

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.

Canada’s inflation-control targets establish a specific medium-term objective for monetary policy.

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

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.