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In this article, the authors build on the framework used in the Bank of Canada's Financial System Review to assess the evolution of household indebtedness and financial vulnerabilities in response to changing economic conditions. To achieve this, they first compare two microdata sets generated by Ipsos Reid's Canadian Financial Monitor and Statistics Canada's Survey of Financial Security. They find that the surveys are broadly comparable, despite methodological differences. This enables them to use the combined information content for the identification of the threshold value of the debt-service ratio (DSR). The article then presents an innovative framework that uses household-level microdata to simulate changes in the distribution of the DSR under various stress scenarios. The authors show how this framework can be used by analyzing the effects of two different scenarios on the distribution of the debt-service ratio and the impact on vulnerable households. This tool will enable researchers to refine their analyses of current risks to the financial health of Canadian households. The article concludes with comments on future directions for refining the Bank's analyses of household sector risk.

We study how input-output networks affect the speed of technology adoption. In particular, we model the decision to adopt the programming language Python 3 by software packages. Python 3 provides advanced features but is not backward compatible with Python 2, which implies it comes with adoption costs.

We simulate introducing a central bank digital currency (CBDC) and consider consumer adoption, merchant acceptance and usage at the point of sale. Modest adoption frictions significantly inhibit CBDC market penetration along all three dimensions. Incumbent responses to restore pre-CBDC market shares are moderate to small and further reduce the impact of a CBDC.

This study uses the COVID-19 travel restrictions to estimate foreign demand for Canadian banknotes. It reveals that international visitors accounted for about 10% of all $100 CAD notes in circulation pre-pandemic, with each visitor carrying an average of $165 in hundred-dollar bills.

We use narrative evidence along with a novel database of real-time data and forecasts from the Bank of Canada's staff economic projections from 1974 to 2015 to construct a new measure of monetary policy shocks and estimate the effects of monetary policy in Canada.

As a guide for the conduct of monetary policy, most central banks make use of a trend inflation index similar to that employed by the Bank of Canada: the CPI excluding food, energy, and the effect of indirect taxes. In addition to their basic reference index, some central banks regularly publish statistical measures of the trend rate of inflation. The method used for producing these measures is, for the most part, based on the hypothesis that extreme price fluctuations generally reflect temporary shocks to the inflation rate, rather than its underlying trend. In this paper, the author offers a broad survey of studies on the measurement of trend inflation that have been published by the Bank of Canada and presents the results of the most recent work on the subject. Particular attention is paid to two statistical measures that the Bank follows more closely than other measures; namely, the CPIX, a price index that excludes eight of the most volatile CPI components, and CPIW, a measure that retains all the components of the overall index but gives a lower weighting to the most volatile.

One of the most important arguments in favour of price stability is that unexpected inflation generates changes in the distribution of income and wealth among different economic agents. These redistributions occur because many loans are specified in fixed dollar terms and unexpected inflation redistributes wealth from creditors to debtors by reducing the real value of nominal assets and liabilities.

We investigate the uncertainty around stock returns at different investment horizons. Since a return is either a loss or a gain, we categorize return uncertainty into two components—loss uncertainty and gain uncertainty. We then use these components to evaluate investment.

Forecasts of the price of crude oil play a significant role in the conduct of monetary policy, especially for commodity producers such as Canada. This article presents a range of recently developed forecasting models that, when pooled together, can generate, on average, more accurate forecasts of the price of oil than the oil futures curve. It also illustrates how policy-makers can evaluate the risks associated with the baseline oil price forecast and how they can determine the causes of past oil price fluctuations.