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In Monitoring Shadow Banking in Canada: A Hybrid Approach, Bo Young Chang, Michael Januska, Gitanjali Kumar and André Usche discuss how lending that occurs outside the traditional banking system provides benefits to the economy but must be monitored carefully for potential financial sector vulnerabilities. They describe how the Bank defines and measures shadow banking and how it assesses vulnerabilities in the sector, using an approach that examines both markets and entities.

We explain how housing supply elasticities for Canadian cities are estimated. The procedure we use exploits the systematic differences in various cities’ sensitivity to regional house-price cycles.

This article reviews recent work that uses principal-component analysis to extract information common to indicators from the Bank of Canada’s Business Outlook Survey (BOS). The authors use correlation analysis and an out-of-sample forecasting exercise to assess and compare the information content of the principal component with that of responses to key individual survey questions on growth in real gross domestic product and in real business investment. Results suggest that summarizing the common movements among BOS indicators may provide useful information for forecasting near-term growth in business investment. For growth in real gross domestic product, however, the survey’s balance of opinion on future sales growth appears to be more informative.

This paper examines the reliability of survey data on business incomes, valuations, and rates of return, which are key inputs for studies of wealth inequality and entrepreneurial choice.

We report on the dynamics of Bitcoin awareness and ownership from 2016 to 2021, using the Bank of Canada's Bitcoin Omnibus Surveys (BTCOS). Our analysis also helps understand Bitcoin owners who adopted during the COVID-19 and how they differ from long-term owners. 

The formulation of monetary policy at the Bank of Canada relies on the analysis of a broad set of economic information. Greater availability of immediate and detailed information would improve real-time economic decision making. Technological advances have provided an opportunity to exploit “big data” - the vast amount of digital data from business transactions, social media and networked computers. Big data can be a complement to traditional information sources, offering fresh insight for the monitoring of economic activity and inflation.

Standard theories of price adjustment are based on the problem of a single-product firm, and therefore they may not be well suited to analyze price dynamics in the economy with multiproduct firms.

When countries are hit by supply shocks, central banks often face the dilemma of either looking through such shocks or reacting to them to ensure that inflation expectations remain anchored. In this paper, we propose a tractable framework to capture this dilemma and then explore optimal policy under a range of assumptions about how expectations are formed.

Do bond risk premiums influence the effects of debt maturity operations? Using a model with realistic bond risk premiums, we show that maturity operations have sizable effects on expected inflation and output when the central bank passively responds to inflation and the fiscal authority weakly responds to the debt level.

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.