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Although many countries have abandoned monetary targeting in recent decades, monetary aggregates are still useful indicators of future economic activity. Past research has shown that, compared with other monetary aggregates and expressed in real terms, net M1 and gross M1 have traditionally provided superior leading information for output growth.

We introduce a history of macroprudential policy (MPP) events in Canada since the 1980s. We document the short-run effects of MPP announcements on market-based measures of systemic risk and find that MPPs can influence the market’s perception of large banks’ resilience.

The financial crisis of 2007-09 and the subsequent extended period of historically low real interest rates have revived the question of whether economic agents are willing to take on more risk when interest rates remain low for a prolonged time period. This increased appetite for risk, which causes economic agents to search for investment assets and strategies that generate higher investment returns, has been called the risk-taking channel of monetary policy. Recent academic research on banks suggests that lending policies in times of low interest rates can be consistent with the existence of a risk-taking channel of monetary policy in Europe, South America, the United States and Canada. Specifically, studies find that the terms of loans to risky borrowers become less stringent in periods of low interest rates. This risk-taking channel may amplify the effects of traditional transmission mechanisms, resulting in the creation of excessive credit.

A series of major international financial crises in the 1990s, and the recent introduction of the euro, have renewed interest in alternative exchange rate systems. The choice of exchange rate regime is particularly relevant for emerging-market countries because other countries are perceived either as having no alternative to their current exchange rate arrangement or as highly unlikely to change. This article examines the evolution of exchange rate regimes in emerging markets over the past decade and compares the strengths and weaknesses of the various available systems. These include intermediate regimes, such as the adjustable pegged exchange rate popular throughout much of the post—war period, and the two extreme exchange rate regimes: permanently fixed or freely floating exchange rate regimes. Two recently proposed alternatives are also evaluated: the Managed Floating Plus and Baskets, Bands, and Crawling Pegs. Both try to combine the best elements of the flexible and fixed exchange rate systems, but the Managed Floating Plus is deemed to be the more promising alternative.

We consider two trade-offs inherent to extraordinary forward guidance (EFG): potency versus flexibility, and the credibility of forward guidance versus the credibility of inflation targeting. We argue that the form of EFG used by the Bank of Canada during the COVID‑19 pandemic balanced both trade-offs relatively well.

We compare the Federal Reserve’s asset purchase programs with those implemented by the Bank of England and the Swedish Riksbank, and the Swiss National Bank’s reserve expansion program.

The third strategy employed by the Bank when dealing with uncertainty is the consideration of appropriate simple reaction functions or "rules" for the setting of the policy interest rate. Since John Taylor's presentation of his much-discussed rule, research on simple policy rules has exploded. Simple rules have several advantages. In particular, they are easy to construct and communicate and are believed by some to be robust, in the sense of generating good results in a variety of economic models. This article provides an overview of the recent research regarding the usefulness and robustness of simple monetary policy rules, particularly in models of the Canadian economy. It also describes and explains the role of simple rules in the conduct of monetary policy in Canada.

We use a new real-time database for Canada to study various output gap measures. This includes recently developed measures based on models incorporating many variables as inputs (and therefore requiring real-time data for many variables).