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The theme of the conference, "managing the recovery," is particularly timely: As we move past the gravest dangers of the financial crisis toward better days, attention has turned to the policy challenges posed by the recovery.

Price controls, or caps, can lead to shortages, as 1970’s gasoline price controls illustrate. One million trades show that the market for borrowing bonds in Canada has an implicit price cap: traders are willing to pay no more than the overnight interest rate to borrow a bond. This suggests the probability of a shortage increases when interest rates are very low.

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.

This paper examines the contribution of several supply factors to US headline inflation since the start of the COVID-19 pandemic. We identify six supply shocks using a structural VAR model: labor supply, labor productivity, global supply chain, oil price, price mark-up and wage mark-up shocks.

Feedback rules are rules aimed at guiding policy-makers as they face the problem of keeping inflation close to a desired path without causing variability elsewhere in the economy. These rules link short-term interest rates, controlled by the central bank, to the rate of inflation and/or its deviation from a target rate. The authors describe the most popular types of feedback rules and review some simulation results.

Model-based forecasts of important economic variables are part of the range of information considered for monetary policy decision making.

This article describes experimental economics, in general, and new developments in experimental macroeconomics, in particular. The approach has a clear niche in providing evidence on economic phenomena that cannot be observed directly or that are difficult to measure. Experimental work conducted by Bank of Canada economists has shed light on a number of issues important to monetary policy, such as the relative efficacy between price-level and inflation targeting, and the nature of inflation expectations formation.

Governor Tiff Macklem talks about the COVID-19 pandemic and major economic forces are affecting the labour market, and the need for all groups to benefits from the recovery.

In its role as fiscal agent to the government, the Bank of Canada provides analysis and advice on decisions about the government's domestic debt portfolio. Debt-management decisions depend on assumptions about future interest rates, macroeconomic outcomes, and fiscal policy, yet when a debt-strategy decision is taken, none of these factors can be known with certainty. Moreover, the government has various financing options (i.e., treasury bills, nominal bonds, and inflation-linked bonds) to meet its objectives of minimizing debt-service charges while simultaneously ensuring a prudent risk profile and well-functioning government securities markets. Bank of Canada staff have therefore developed a mathematical model to assist in the decision-making process. This article describes the key aspects of the debt manager's challenge and the principal assumptions incorporated in the debt-strategy model, illustrated with specific results.

To what extent does a central bank digital currency (CBDC) compete with bank deposits? To answer this question, we develop and estimate a structural model where each household chooses which financial institution to deposit their digital money with.