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In this article the author examines the effects that major changes in energy prices in recent years have had on inflation and on the pace of economic expansion. These are then compared with the effects of the oil-price shocks that occurred in the 1970s and early 1980s. Changes in the intensity of energy use are examined, as well as developments in Canada's merchandise trade surplus in energy commodities and products. The author also considers the effects that a monetary policy anchored to low and stable inflation could have on price-setting behaviour and thus on the pass-through of higher energy costs to core inflation in Canada and in other industrial countries.

Consumers often express concerns about lack of privacy, but they still give up a lot of data to digital platforms. This paper builds a dynamic game-theoretic model of data collection and privacy protection, which potentially explains consumers’ behaviour.

I quantify the importance of changes in household preferences on household inflation rates using 11 years of scanner data for 11,000 US households. My results suggest that changes in household preferences are an important driver of inflation dynamics at the household level.

The disruption due to COVID-19 reverberated through the bond markets in three phases. In the first phase, dealers met the rising demand for liquidity. In the second, dealers reduced the supply of liquidity, and trading conditions worsened significantly. Finally, the market returned to relative stability following several interventions by the Bank of Canada.

Since the 2016 summer survey, the results from a question on future sales indicators (FSI) have been included in the Business Outlook Survey (BOS). This backgrounder briefly describes the question and presents the correlations between the responses and various measures of business activity.

Risk-management systems in financial institutions have come under increasing scrutiny in light of the current financial crisis, resulting in calls for improvements and an increased role for regulators. Yet such objectives miss the intricacy at the heart of the risk-management process. This article outlines the complexity inherent in any modern risk-management system, which arises because there are shortcuts in the theoretical models that risk managers need to be aware of, as well as the difficulties in sensible calibration of model parameters. The author suggests that prudential regulation of such systems should focus on failures within the financial firm and in the market interactions between firms and reviews possible strategies that can improve the performance of risk management and microprudential regulatory practice.

The industry standard for seasonally adjusting data, X-13ARIMA-SEATS, is not suitable for high-frequency data. We summarize and assess several of the most popular seasonal adjustment methods for weekly data given the increased availability and promise of non-traditional data at higher frequencies.

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.

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.